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  • 1.
    Bi, Z. M.
    et al.
    Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China.;Purdue Univ, Dept Civil & Mech Engn, Ft Wayne, IN 46805 USA..
    Luo, Chaomin
    Mississippi State Univ, Dept Elect & Comp Engn, Mississippi State, MS 39762 USA..
    Miao, Zhonghua
    Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China..
    Zhang, Bing
    Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China..
    Zhang, W. J.
    Univ Saskatchewan, Dept Mech Engn, Saskatoon, SK S7N 5A9, Canada..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. KTH Royal Inst Technol, Dept Prod Engn, S-10044 Stockholm, Sweden..
    Safety assurance mechanisms of collaborative robotic systems in manufacturing2021In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 67, article id 102022Article in journal (Refereed)
    Abstract [en]

    Collaborative robots (cobots) are robots that are designed to collaborate with humans in an open workspace. In contrast to industrial robots in an enclosed environment, cobots need additional mechanisms to assure humans' safety in collaborations. It is especially true when a cobot is used in manufacturing environment; since the workload or moving mass is usually large enough to hurt human when a contact occurs. In this article, we are interested in understanding the existing studies on cobots, and especially, the safety requirements, and the methods and challenges of safety assurance. The state of the art of safety assurance of cobots is discussed at the aspects of key functional requirements (FRs), collaboration variants, standardizations, and safety mechanisms. The identified technological bottlenecks are (1) acquiring, processing, and fusing diversified data for risk classification, (2) effectively updating the control to avoid any interference in a real-time mode, (3) developing new technologies for the improvement of HMI performances, especially, workloads and speeds, and (4) reducing the overall cost of safety assurance features. To promote cobots in manufacturing applications, the future researches are expected for (1) the systematic theory and methods to design and build cobots with the integration of ergonomic structures, sensing, real-time controls, and human-robot interfaces, (2) intuitive programming, task-driven programming, and skill-based programming which incorporate the risk management and the evaluations of biomechanical load and stopping distance, and (3) advanced instrumentations and algorithms for effective sensing, processing, and fusing of diversified data, and machine learning for high-level complexity and uncertainty. The needs of the safety assurance of integrated robotic systems are specially discussed with two development examples.

  • 2. Bi, Z.M.
    et al.
    Wang, Lihui
    University of Skövde.
    Advances in 3D Data Acquisition and Processing for Industrial Applications2010In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 26, no 5, p. 403-413Article in journal (Refereed)
    Abstract [en]

    A critical task of vision-based manufacturing applications is to generate a virtual representation of a physical object from a dataset of point clouds. Its success relies on reliable algorithms and tools. Many effective technologies have been developed to solve various problems involved in dataacquisition and processing. Some articles are available on evaluating and reviewing these technologies and underlying methodologies. However, for most practitioners who lack a strong background on mathematics and computer science, it is hard to understand theoretical fundamentals of the methodologies. In this paper, we intend to survey and evaluate recent advances in data acquisition and progressing, and provide an overview from a manufacturing perspective. Some potential manufacturing applications have been introduced, the technical gaps between the practical requirements and existing technologies discussed, and research opportunities identified.

  • 3. Bi, Z.M.
    et al.
    Wang, Lihui
    University of Skövde.
    Dynamic Control Model of a Cobot with Three Omni-Wheels2010In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 26, no 6, p. 558-563Article in journal (Refereed)
    Abstract [en]

    In this paper, a new collaborative robot with omni-wheels has been proposed and its dynamic control has been developed and validated. Collaborative robots (Cobots) have been introduced to guide and assist human operators to move heavy objects in a given trajectory. Most of the existing cobots use steering wheels: typical drawbacks of using steering wheels include the difficulties to (i) follow a trajectory with a curvature larger than that of the base platform, (ii) mount encoders on steering wheels due to self-spinning of the wheels, and (iii) quarantine dynamic control performance since it is purely kinematic control. The new collaborative robot is proposed to overcome the above-mentioned shortcomings. The methodologies for its dynamic control are focused and the simulation has been conducted to validate the control performance of the system.

  • 4. Bi, Z.M.
    et al.
    Wang, Lihui
    University of Skövde.
    Optimal Design of Reconfigurable Parallel Machining Systems2009In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 25, no 6, p. 951-961Article in journal (Refereed)
    Abstract [en]

    A reconfigurable machining system is usually a modularized system, and its configuration design concerns the selections of modules and the determination of geometric dimensions in some specific modules. All of its design perspectives from kinematics, dynamics, and control have to be taken into considerations simultaneously, and a multidisciplinary design optimization (MDO) tool is required to support the configuration design process. This paper presents a new MDO tool for reconfigurable machining systems, and it includes the following works: (i) the literatures on the computer-aided design of reconfigurable parallel machining systems have been reviewed with a conclusion that the multidisciplinary design optimization is essential, but no comprehensive design tool is available to reconfigurable parallel machining systems; (ii) a class of reconfigurable systems called reconfigurable tripod-based machining system has been introduced, its reconfiguration problem is identified, and the corresponding design criteria have been discussed; (iii) design analysis in all of the disciplines including kinematics, dynamics, and control have been taken into considerations, and design models have been developed to evaluate various design candidates; in particular, the innovative solutions to direct kinematics, stiffness analysis for the design configurations of tripod-based machines with a passive leg, and concise dynamic modelling have been provided; and (iv) A design optimization approach is proposed to determine the best solution from all possible configurations. Based on the works presented in this paper, a computer-aided design and control tool have been implemented to support the system reconfiguration design and control processes. Some issues relevant to the practical implementation have also been discussed.

  • 5. Fan, W.
    et al.
    Zheng, L.
    Ji, W.
    Xu, X.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Lu, Y.
    Zhao, X.
    Function block-based closed-loop adaptive machining for assembly interfaces of large-scale aircraft components2020In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 66, article id 101994Article in journal (Refereed)
    Abstract [en]

    To guarantee the docking accuracy of large-scale components, their assembly interfaces usually need to be finished before the final assembly. However, there are some crucial problems affecting finishing efficiency and quality, e.g. use of hard-to-machine material at the assembly interface, manual interventions and process diversity in finish machining, difficulties in the alignment of the large component, as well as errors between the as-built and as-designed status of the large component. These problems significantly enhance the uncertainty in finish machining on a shop floor. To solve these problems, this paper proposes an approach of adaptive process planning and execution, i.e., IEC 61499 Function Block (FB) based Closed-Loop Adaptive Machining (CLAM). Thus, the adaptive alignment of the large component is achieved, which can guarantee the correct location between the assembly interface and the cutting tool. As well as the on-line CLAM of the assembly interface is also realized to improve the machining efficiency and quality. As a result, a FB based CLAM system for the assembly interfaces is established, which contains a CAD system, a FB enabled High-Level Controller (HLC), and several Low-Level Controllers (LLCs), as well as a mechanical system. The most notable is that the related FBs are designed to plan and execute the finishing process. Finally, the proposed method and system are validated by a large component from a real aviation industry, i.e., a vertical tail of a passenger aircraft. The experimental results indicate that the proposed method and system are feasible and effective to address the above-mentioned problems.

  • 6.
    Fan, Wei
    et al.
    Beihang Univ, Sch Mech Engn & Automat, Beijing 100191, Peoples R China.;Univ Auckland, Dept Mech Engn, Auckland 1142, New Zealand..
    Zheng, Lianyu
    Beihang Univ, Sch Mech Engn & Automat, Beijing 100191, Peoples R China..
    Ji, Wei
    AB Sandvik Coromant, S-12679 Stockholm, Sweden..
    Xu, Xun
    Univ Auckland, Dept Mech Engn, Auckland 1142, New Zealand..
    Lu, Yuqian
    Univ Auckland, Dept Mech Engn, Auckland 1142, New Zealand..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A machining accuracy informed adaptive positioning method for finish machining of assembly interfaces of large-scale aircraft components2021In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 67, article id 102021Article in journal (Refereed)
    Abstract [en]

    An assembly interface of a large-scale aircraft component is a joint surface to connect adjacent large components. To guarantee the final assembly accuracy of the large components, the assembly interface is finish machined on site before the final assembly to cut the observed machining allowance. Thus, aiming at realizing the high efficiency and high quality in the finish machining operation, in this paper we propose an adaptive positioning method that integrates comprehensive engineering constrains (including Positioning Accuracy Constraints (PACs) of the large component and Machining Accuracy Constraints (MACs) of the assembly interface). In this method, the key Measurement Points (MPs) of a component are assigned to obtain its initial pose. Then the measurement data and the initial pose are used as input data to obtain the optimal pose parameters of the component based on an improved Particle Swarm Optimization Simulated Annealing (PSO-SA) algorithm. The optimal pose parameters can provide data support for the adaptive positioning of the large component, the function of which is implemented based on IEC 61499 Function Block (FB) technology. Finally, a positioning experiment of a vertical tail of a large passenger aircraft is used to validate the proposed method. The experimental results illustrate that the proposed method can improve the efficiency and positioning accuracy of the large component, compared to the traditional method.

  • 7.
    Fang, Wei
    et al.
    Beijing Univ Posts & Telecommun, Sch Automation, Beijing, Peoples R China..
    Chen, Lixi
    Beijing Univ Posts & Telecommun, Sch Automation, Beijing, Peoples R China..
    Zhang, Tienong
    Beijing Univ Posts & Telecommun, Sch Automation, Beijing, Peoples R China..
    Chen, Chengjun
    Qingdao Univ Technol, Sch Mech & Automot Engn, Qingdao, Peoples R China..
    Teng, Zhan
    Beijing Univ Posts & Telecommun, Sch Automation, Beijing, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Head-mounted display augmented reality in manufacturing: A systematic review2023In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 83, article id 102567Article, review/survey (Refereed)
    Abstract [en]

    Head-mounted display (HMD) augmented reality (AR) has attracted more and more attention in manufacturing activities, as it enables operators to access visual guidance in front of their view directly while freeing human's two hands. Nevertheless, HMD AR has not been widely adopted in manufacturing fields as humans expected since the release of Google Glass in 2012, and thus it is important to understand the related issues arising from the actual deployments of HMD AR on the shop floor. To the best of the authors' knowledge, there have not been comprehensive discussions on HMD AR in manufacturing from a holistic perspective. This article aims to provide an extensive map for the distribution of HMD AR in various manufacturing activities and a systematic overview of underlying technical perspectives associated with their actual industrial applications between 2010 and 2022, involving AR visualization, tracking and registration, context awareness, human-machine interaction, as well as ergonomics and usability, which are significant for the actual AR deployments for human-centric manufacturing in Industry 5.0. It is also worth mentioning that this work presents a historical overview of the current research on the development of HMD AR, as well as a summary of the existing methods and open problems for HMD AR in manufacturing. It is helpful to understand the current technical situations of HMD AR while providing insights to deploy industrial AR applications and perform academic research in the future.

  • 8.
    Fang, Wei
    et al.
    Beijing Univ Posts & Telecommun, Sch Modern Post, Sch Automat, Beijing, Peoples R China..
    Fan, Wei
    Beihang Univ, Sch Mech Engn & Automat, Beijing, Peoples R China..
    Ji, Wei
    Sandv Coromant, Dept Digital Machining, Stockholm, Sweden..
    Han, Lei
    Beijing Univ Posts & Telecommun, Sch Modern Post, Sch Automat, Beijing, Peoples R China..
    Xu, Shuhong
    COMAC Beijing Aircraft Technol Res Inst, Beijing, Peoples R China..
    Zheng, Lianyu
    Beihang Univ, Sch Mech Engn & Automat, Beijing, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Distributed cognition based localization for AR-aided collaborative assembly in industrial environments2022In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 75, article id 102292Article in journal (Refereed)
    Abstract [en]

    The existing (augmented reality) AR-aided assembly is highly associated with AR devices, which mainly provides guidance for one operator, and it is hard to share augmented assembly instructions for large-scale products which require multiple operators working together. To address this problem, the paper proposes a distributed cognition based localization method for AR-aided collaborative assembly. Firstly, a scene cognition using multi-view acquisition about industrial environments is performed with incremental modeling in advance, providing the foundation for the subsequent pose estimate of multi-AR clients. Then, based on feature extracting and matching against the pre-built shop floor model, a pose recovery of AR-aided system is derived from different views of AR operators in a global coordinate system, followed by a distributed motion tracking with the complementary features of visual and inertial data, resulting in a co-located collaborative AR instruction for assembly. Finally, experiments are carried out to validate the proposed method, and experimental results illustrate that the proposed method can achieve distributed cognition-based localization accurately and robustly. Therefore, shared visual communications among multiple operators are synchronized, and assembly status is aware by all the operators.

  • 9. Givehchi, M.
    et al.
    Ng, A.H.C.
    Wang, Lihui
    University of Skövde.
    Spot-Welding Sequence Planning and Optimization Using a Hybrid Rule-Based Approach and Genetic Algorithm2011In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 27, no 4, p. 714-722Article in journal (Refereed)
    Abstract [en]

    Performing assembly planning to find a valid hierarchical assembling structure of a product (i.e. Manufacturing Bill of Materials or MBOM) based on the constraints and necessities inferred from or declared by different sources is potentially complicated. On the other hand, Engineering Changes (EC) may drastically affect the constraints and necessities which the planning of an MBOM was based on. Managing ECs to evaluate and propagate their effects on the upstream data used in assembly planning and downstream activities and information is crucial but problematic. Often it is possible to define a set of rules for the constraints and necessities of assembly planning and find solutions or check validity of solutions based on the rule-set. This paper proposes a rule-based assembly planning method and introduces the concepts and standard notations on how structured rule-sets can be derived from descriptive rules and then used in an algorithm for generating or validating MBOMs. The method was partially automated and successfully employed along with a commercial Virtual Manufacturing package integrated with an in-house developed GA-based sequence optimizer and applied to the sequence optimization in minimizing the cycle time of the robotic spot welding operations for a sheet-metal assembly found in automotive industry.

  • 10.
    Givehchi, Mohammad
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Haghighi, Azadeh
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Cloud-DPP for distributed process planning of mill-turn machining operations2017In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 47, p. 76-84Article in journal (Refereed)
    Abstract [en]

    Today, the dynamic market requires manufacturing firms to possess a high degree of adaptability to deal with shop-floor uncertainties. Specifically targeting SMEs active in the metal cutting sector who normally deal with intensive process planning problems, researchers have tried to address the subject. Among proposed solutions, Cloud-DPP elaborates a two-layer distributed adaptive process planning based on function-block technology and cloud concept. One of the challenges of companies is to machine as many part features as possible in a single setup on a single machine. Nowadays, multi-tasking machines are widely used due to their various advantages such as reducing setup times and increasing part accuracy. However, they also possess programming challenges because of their complex configuration and multiple machining functions. This paper reports the latest state of design and implementation of Cloud-DPP methodology to support parts with a combination of milling and turning features, and process planning for multi-tasking machining centers with special functionalities to minimize the number of setups. The contributions of this work are: representation of machining states and part transfer functionality, support of multi-tasking machines in adaptive setup merging, development of special function blocks to handle sub-setups and transitions, and finally generation of function block network for the merged setups. The developed prototype is validated through a case study.

  • 11.
    Gonzalez, Monica
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Manufacturing and Metrology Systems.
    Theissen, Nikolas Alexander
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Manufacturing and Metrology Systems.
    Barrios, Asier
    Archenti, Andreas
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Manufacturing and Metrology Systems.
    Online compliance error compensation system for industrial manipulators in contact applications2022In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 76, p. 102305-102305, article id 102305Article in journal (Refereed)
    Abstract [en]

    Industrial manipulators are rarely used in high-force processes even though they provide flexibility, adaptability, relatively low cost, and a large workspace. This limited utilization is mainly due to their inherent low stiffness, which results in significant deformation. Hence, it is necessary to improve their accuracy in order to achieve high-precision requirements while performing tasks under load. This paper focuses on the development and implementation of an online compliance error compensation system for industrial manipulators. The proposed algorithm computes the compensation based on an elasto-geometric robot model and process forces measured with a force sensor mounted between the robot mechanical interface and the end effector. The performance of the compensation system is evaluated experimentally in two high payload robots from different manufacturers in which the compensation was carried out to reduce the mean deformation of circular trajectories under load.

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  • 12.
    Hua, Jiaqi
    et al.
    Nanjing Univ Aeronaut & Astronaut, Natl Key Lab Sci & Technol Helicopter Transmiss, Coll Mech & Elect Engn, Nanjing 210016, Peoples R China..
    Li, Yingguang
    Nanjing Univ Aeronaut & Astronaut, Natl Key Lab Sci & Technol Helicopter Transmiss, Coll Mech & Elect Engn, Nanjing 210016, Peoples R China..
    Liu, Changqing
    Nanjing Univ Aeronaut & Astronaut, Natl Key Lab Sci & Technol Helicopter Transmiss, Coll Mech & Elect Engn, Nanjing 210016, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    A zero-shot prediction method based on causal inference under non-stationary manufacturing environments for complex manufacturing systems2022In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 77, article id 102356Article in journal (Refereed)
    Abstract [en]

    The state prediction of key components in manufacturing processes plays an important role in intelligent manufacturing, as it could improve the production quality, efficiency and reduce costs. Data-driven methods could learn well-performed prediction models from large volume of data. However, in complex manufacturing systems, the lack of prior knowledge limits the performance of prediction models, where the manufacturing environments changes continuously. In order to address this issue, this paper proposed a zero-shot prediction method for complex manufacturing systems based on causal inference. A deep convolutional neural network and a causal representation model are jointly optimized to extract invariant causal signal features, which can be generalized to non-stationary manufacturing environments without any new data. The experiment of tool wear prediction under non-stationary working conditions is carried out as a research example. The proposed method is verified with the open dataset on tool wear prediction, and experimental results show that the prediction accuracy could be obviously improved over existing methods.

  • 13.
    Ji, Qinglei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Chen, Mo
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Feng, Lei
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Machine Design (Div.).
    Optimal shape morphing control of 4D printed shape memory polymer based on reinforcement learning2022In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 73, article id 102209Article in journal (Refereed)
    Abstract [en]

    4D printing technology, as a new generation of Additive Manufacturing methods, enables printed objectsto further change their shapes or other properties upon external stimuli. One main category of 4D printingresearch is 4D printed thermal Shape Memory Polymer (SMP). Its morphing process has large time delay, isnonlinear time variant, and susceptible to unpredictable disturbances. Reaching an arbitrary position with highprecision is an active research question. This paper applies the Reinforcement Learning (RL) method to developan optimal control method to perform closed loop control of the SMP actuation. Precise and prompt shapemorphing is achieved compared with previous control methods using a PI controller. The training efforts of RLare further reduced by simplifying the optimal control policy using the structural property of the prior trainedresults. Customized protective visors against COVID-19 are fabricated using the proposed control method.

  • 14.
    Ji, Qinglei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Fu, Shuo
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Tan, Kaige
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Thorapalli Muralidharan, Seshagopalan
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Lagrelius, Karin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science.
    Danelia, David
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Andrikopoulos, Georgios
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Feng, Lei
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
    Synthesizing the optimal gait of a quadruped robot with soft actuators using deep reinforcement learning2022In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 78, p. 102382-102382, article id 102382Article in journal (Refereed)
    Abstract [en]

    Quadruped robots have the advantages of traversing complex terrains that are difficult for wheeled robots. Most of the reported quadruped robots are built by rigid parts. This paper proposes a new design of quadruped robots using soft actuators driven by tendons as the four legs. Compared to the rigid robots, the proposed soft quadruped robot has inherent safety, less weight and simpler mechanism for fabrication and control, but the corresponding challenge is that the accurate mathematical model applicable to model-based control design of the soft robot is difficult to derive by dynamics. To synthesize the optimal gait controller of the soft-legged robot, the paper makes the following contributions. First, the flexible components of the quadruped robot are modeled with different finite element and lumped parameter methods. The model accuracy and computation efficiency are analyzed. Second, soft actor–critic methods and curriculum learning are applied to learn the optimal gaits for different walking tasks. Third, The learned gaits are implemented in an in-house robot to transport hand tools. Preliminary results show that the robot can walk forward and correct the walking directions.

  • 15. Keshavarzmanesh, Shadi
    et al.
    Wang, Lihui
    University of Skövde.
    Feng, Hsi-Yung
    A Hybrid Approach for Dynamic Routing Planning in an Automated Assembly Shop2010In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 26, no 6, p. 768-777Article in journal (Refereed)
    Abstract [en]

    Highly turbulent environment of dynamic job-shop operations affects shop floor layout as well as manufacturing operations. Due to the dynamic nature of layout changes, essential requirements such as adaptability and responsiveness to the changes need to be considered in addition to the cost issues of material handling and machine relocation when reconfiguring a shop floor's layout. Here, based on the source of uncertainty, the shop floor layout problem is split into two sub-problems and dealt with by two modules: re-layout and find-route. GA is used where changes cause the entire shop re-layout, while function blocks are utilised to find the best sequence of robots for the new conditions within the existing layout. This paper reports the latest development to the authors' previous work

  • 16.
    Li, Shufei
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering. Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China..
    Zheng, Pai
    Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China..
    Liu, Sichao
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Wang, Zuoxu
    Beihang Univ, Sch Mech Engn & Automat, Beijing, Peoples R China..
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Zheng, Lianyu
    Beihang Univ, Sch Mech Engn & Automat, Beijing, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Proactive human-robot collaboration: Mutual-cognitive, predictable, and self-organising perspectives2023In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 81, p. 102510-, article id 102510Article, review/survey (Refereed)
    Abstract [en]

    Human-Robot Collaboration (HRC) has a pivotal role in smart manufacturing for strict requirements of human -centricity, sustainability, and resilience. However, existing HRC development mainly undertakes either a human-dominant or robot-dominant manner, where human and robotic agents reactively perform operations by following pre-defined instructions, thus far from an efficient integration of robotic automation and human cognition. The stiff human-robot relations fail to be qualified for complex manufacturing tasks and cannot ease the physical and psychological load of human operators. In response to these realistic needs, this paper presents our arguments on the obvious trend, concept, systematic architecture, and enabling technologies of Proactive HRC, serving as a prospective vision and research topic for future work in the human-centric smart manufacturing era. Human-robot symbiotic relation is evolving with a 5C intelligence - from Connection, Coordination, Cyber, Cognition to Coevolution, and finally embracing mutual-cognitive, predictable, and self -organising intelligent capabilities, i.e., the Proactive HRC. With proactive robot control, multiple human and robotic agents collaboratively operate manufacturing tasks, considering each others' operation needs, desired resources, and qualified complementary capabilities. This paper also highlights current challenges and future research directions, which deserve more research efforts for real-world applications of Proactive HRC. It is hoped that this work can attract more open discussions and provide useful insights to both academic and industrial practitioners in their exploration of human-robot flexible production.

  • 17.
    Li, Xiaobin
    et al.
    College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, China.
    Zhang, Shucheng
    College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, China.
    Jiang, Pei
    College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, China.
    Deng, Mikun
    College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, China.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Yin, Chao
    College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400044, China.
    Knowledge graph based OPC UA information model automatic construction method for heterogeneous devices integration2024In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 88, article id 102736Article in journal (Refereed)
    Abstract [en]

    Emergent manufacturing paradigms, including ubiquitous manufacturing, social manufacturing, and agile manufacturing, facilitate the advancement of the manufacturing industry. However, these paradigms suffer from the difficulty of interoperability due to the heterogeneity of field devices and diverse communication protocols. The OLE for Process Control Unified Architecture (OPC UA) technique, which is featured with cross-platform, is considered as the pivotal technology for addressing this issue. However, there still exist a large number of heterogeneous devices which do not support OPC UA protocol. The communication protocols of these heterogeneous devices need to be converted into OPC UA based on information model, nevertheless, the manual construction of OPC UA information model is cumbersome and inefficient. In this paper, an integrated architecture based on reasoning over the OPC UA information model is proposed to realize rapid integration of heterogeneous devices to achieve interoperability. To realize the proposed integrated architecture, an automatic device information model construction method is developed simultaneously. The method first identifies the type of the newly accessed device based on the character-level TextCNN (CTCNN) model, which utilizes the word sequence extracted from the corresponding device data frame as input. Subsequently an open-world knowledge completion model is adopted to link the unseen entities in the device data frame to the information model knowledge graph(KG) to support the information model automatic construction for unknown device. For evaluation purpose, two industrial datasets are constructed and the results demonstrate the feasibility and effectiveness of the proposed method.

  • 18.
    Li, Xuebing
    et al.
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Yue, Caixu
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Liu, Xianli
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Zhou, Jiaqi
    Harbin Univ Sci & Technol, Key Lab Adv Mfg & Intelligent Technol, Minist Educ, Harbin 150080, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    ACWGAN-GP for milling tool breakage monitoring with imbalanced data2024In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 85, article id 102624Article in journal (Refereed)
    Abstract [en]

    Tool breakage monitoring (TBM) during milling operations is crucial for ensuring workpiece quality and mini-mizing economic losses. Under the premise of sufficient training data with a balanced distribution, TBM methods based on statistical analysis and artificial intelligence enable accurate recognition of tool breakage conditions. However, considering the actual manufacturing safety, cutting tools usually work in normal wear conditions, and acquiring tool breakage signals is extremely difficult. The data imbalance problem seriously affects the recog-nition accuracy and robustness of the TBM model. This paper proposes a TBM method based on the auxiliary classier Wasserstein generative adversarial network with gradient penalty (ACWGAN-GP) from the perspective of data generation. By introducing Wasserstein distance and gradient penalty terms into the loss function of ACGAN, ACWGAN-GP can generate multi-class fault samples while improving the network's stability during adversarial training. A sample filter based on multiple statistical indicators is designed to ensure the quality and diversity of the generated data. Qualified samples after quality assessment are added to the original imbalanced dataset to improve the tool breakage classifier's performance. Artificially controlled face milling experiments for TBM are carried out on a five-axis CNC machine to verify the effectiveness of the proposed method. Experimental results reveal that the proposed method outperforms other popular imbalance fault diagnosis methods in terms of data generation quality and TBM accuracy, and can meet the real-time requirements of TBMs.

  • 19.
    Li, Yibing
    et al.
    Wuhan Univ Technol, Sch Mech & Elect Engn, Wuhan, Peoples R China.;Wuhan Univ Technol, Hubei Digital Mfg Key Lab, Wuhan 430070, Peoples R China..
    Tao, Zhiyu
    Wuhan Univ Technol, Sch Mech & Elect Engn, Wuhan, Peoples R China.;Wuhan Univ Technol, Hubei Digital Mfg Key Lab, Wuhan 430070, Peoples R China..
    Wang, Lei
    Wuhan Univ Technol, Sch Mech & Elect Engn, Wuhan, Peoples R China.;Wuhan Univ Technol, Hubei Digital Mfg Key Lab, Wuhan 430070, Peoples R China..
    Du, Baigang
    Wuhan Univ Technol, Sch Mech & Elect Engn, Wuhan, Peoples R China.;Wuhan Univ Technol, Hubei Digital Mfg Key Lab, Wuhan 430070, Peoples R China..
    Guo, Jun
    Wuhan Univ Technol, Sch Mech & Elect Engn, Wuhan, Peoples R China.;Wuhan Univ Technol, Hubei Digital Mfg Key Lab, Wuhan 430070, Peoples R China..
    Pang, Shibao
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Wuhan Univ Technol, Sch Mech & Elect Engn, Wuhan, Peoples R China.;Wuhan Univ Technol, Hubei Digital Mfg Key Lab, Wuhan 430070, Peoples R China..
    Digital twin-based job shop anomaly detection and dynamic scheduling2023In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 79, article id 102443Article in journal (Refereed)
    Abstract [en]

    Scheduling scheme is one of the critical factors affecting the production efficiency. In the actual production, anomalies will lead to scheduling deviation and influence scheme execution, which makes the traditional job shop scheduling methods are not sufficient to meet the needs of real-time and accuracy. By introducing digital twin (DT), further convergence between physical and virtual space can be achieved, which enormously reinforces real-time performance of job shop scheduling. For flexible job shop, an anomaly detection and dynamic scheduling framework based on DT is proposed in this paper. Previously, a multi-level production process monitoring model is proposed to detect anomaly. Then, a real-time optimization strategy of scheduling scheme based on rolling window mechanism is explored to enforce dynamic scheduling optimization. Finally, the improved grey wolf optimization algorithm is introduced to solve the scheduling problem. Under this framework, it is possible to monitor the deviation between the actual processing state and the planned processing state in real time and effectively reduce the deviation. An equipment manufacturing job shop is taken as a case study to illustrate the effectiveness and advantages of the proposed framework.

  • 20. Lian, Binbin
    et al.
    Sun, Tao
    Tianjin University.
    Song, Yimin
    Tianjin Univeristy.
    Parameter sensitivity analysis of a 5-DoF parallel manipulator2017In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537Article in journal (Refereed)
  • 21.
    Lian, Binbin
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Static and dynamic optimization of a pose adjusting mechanism considering parameter changes during construction2019In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 59, p. 267-277Article in journal (Refereed)
    Abstract [en]

    Having potentially high stiffness and good dynamic response, a parallel pose adjusting mechanism was proposed for being an attachment to a big serial robot of a macro-micro robotic system. This paper addresses its design optimization problem mainly concerning arrangements of design variables and objectives. Parameter changes during construction are added to the design variables in order to prevent the negative effects to the physical prototype. These parameter changes are interpreted as parameter uncertainty and modeled by probabilistic theory. For the objectives, both static and dynamic performances are simultaneously optimized by Pareto-based method. The involved performance indices are instantaneous energy based stiffness index, first natural frequency and execution mass. The optimization procedure is implemented as: (1) carrying out performance modeling and defining performance indices, (2) reformulating statistical objectives and probabilistic constraints considering parameter uncertainty, (3) conducting Pareto-based optimization with the aid of response surface method (RSM) and particle swarm optimization (PSO), (4) selecting optimal solution by searching for cooperative equilibrium point (CEP). By addressing parameter uncertainty and the best compromise among multiple objectives, the presented optimization procedure provides more reliable optimal parameters that would not be affected by minor parameter changes during construction, and less biased optimum between static and dynamic performances comparing with the conventional optimization methods. The proposed optimization method can also be applied to the other similar mechanisms.

  • 22. Liang, Huagang
    et al.
    Wen, Xiaoqian
    Liu, Yongkui
    Zhang, Haifeng
    Zhang, Lin
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning2021In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 67, article id 101991Article in journal (Refereed)
    Abstract [en]

    Cloud manufacturing is a new manufacturing model that aims to provide on-demand manufacturing services to consumers over the Internet. Service composition is an essential issue as well as an important technique in cloud manufacturing (CMfg) that supports construction of larger-granularity, value-added services by combining a number of smaller-granularity services to satisfy consumers' complex requirements. Meta-heuristics algorithms such as genetic algorithm, particle swarm optimization, and ant colony algorithm are frequently employed for addressing service composition issues in cloud manufacturing. These algorithms, however, require complex design flows and painstaking parameter tuning, and lack adaptability to dynamic environment. Deep re-inforcement learning (DRL) provides an alternative approach for solving cloud manufacturing service compo-sition (CMfg-SC) issues. DRL as model-free artificial intelligent methods enables a system to learn optimal service composition solutions through training, which can therefore circumvent the aforementioned problems with meta-heuristics algorithms. This paper is dedicated to exploring possible applications of DRL in CMfg-SC. A logistics-involved QoS-aware DRL-based CMfg-SC is proposed. A dueling Deep Q-Network (DQN) with prior-itized replay named PD-DQN is designed as the DRL algorithm. Effectiveness, robustness, adaptability, and scalability of PD-DQN are investigated, and compared with that of the basic DQN and Q-learning. Experimental results indicate that PD-DQN is able to effectively address the CMfg-SC problem.

  • 23.
    Lihui, Yongkui Liu
    et al.
    Xidian Univ, Xian, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Makris, Sotiris
    Univ Patras, Patras, Greece..
    Krueger, Jorg
    Tech Univ Berlin, Berlin, Germany..
    Smart robotics for manufacturing2023In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 82, article id 102535Article in journal (Other academic)
  • 24.
    Liu, Chao
    et al.
    Aston Univ, Coll Engn & Phys Sci, Birmingham B47 ET, England..
    Xu, Xun
    Univ Auckland, Dept Mech Engn, Auckland 1010, New Zealand..
    Gao, Robert X.
    Case Western Reserve Univ, Dept Mech & Aerosp Engn, Cleveland, OH 44106 USA..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Verl, Alexander
    Univ Stuttgart, Inst Control Engn Machine Tools & Mfg, Units ISW, D-70174 Stuttgart, Germany..
    Digitalization and servitization of machine tools in the era of Industry 4.02023In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 83, article id 102566Article in journal (Other academic)
  • 25.
    Liu, Hongyi
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Collision-free human-robot collaboration based on context awareness2021In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 67, article id 101997Article in journal (Refereed)
    Abstract [en]

    Recent advancements in human-robot collaboration have enabled human operators and robots to work together in a shared manufacturing environment. However, current distance-based collision-free human-robot collaboration system can only ensure human safety but not assembly efficiency. In this paper, the authors present a context awareness-based collision-free human-robot collaboration system that can provide human safety and assembly efficiency at the same time. The system can plan robotic paths that avoid colliding with human operators while still reach target positions in time. Human operators' poses can also be recognised with low computational expenses to further improve assembly efficiency. To support the context-aware collision-free system, a complete collision sensing module with sensor calibration algorithms is proposed and implemented. An efficient transfer learning-based human pose recognition algorithm is also adapted and tested. Two experiments are designed to test the performance of the proposed human pose recognition algorithm and the overall system. The results indicate an efficiency improvement of the overall system.

  • 26.
    Liu, Sichao
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production engineering. ABB Corporate Research, Västerås, Sweden.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Gao, Robert X.
    Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, USA.
    Cognitive neuroscience and robotics: Advancements and future research directions2024In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 85, article id 102610Article, review/survey (Refereed)
    Abstract [en]

    In recent years, brain-based technologies that capitalise on human abilities to facilitate human–system/robot interactions have been actively explored, especially in brain robotics. Brain–computer interfaces, as applications of this conception, have set a path to convert neural activities recorded by sensors from the human scalp via electroencephalography into valid commands for robot control and task execution. Thanks to the advancement of sensor technologies, non-invasive and invasive sensor headsets have been designed and developed to achieve stable recording of brainwave signals. However, robust and accurate extraction and interpretation of brain signals in brain robotics are critical to reliable task-oriented and opportunistic applications such as brainwave-controlled robotic interactions. In response to this need, pervasive technologies and advanced analytical approaches to translating and merging critical brain functions, behaviours, tasks, and environmental information have been a focus in brain-controlled robotic applications. These methods are composed of signal processing, feature extraction, representation of neural activities, command conversion and robot control. Artificial intelligence algorithms, especially deep learning, are used for the classification, recognition, and identification of patterns and intent underlying brainwaves as a form of electroencephalography. Within the context, this paper provides a comprehensive review of the past and the current status at the intersection of robotics, neuroscience, and artificial intelligence and highlights future research directions.

  • 27.
    Liu, Sichao
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Sensorless force estimation for industrial robots using disturbance observer and neural learning of friction approximation2021In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 71, p. 1-11, article id 102168Article in journal (Refereed)
    Abstract [en]

    Contact force estimation enables robots to physically interact with unknown environments and to work with human operators in a shared workspace. Most heavy-duty industrial robots without built-in force/torque sensors rely on the inverse dynamics for the sensorless force estimation. However, this scheme suffers from the serious model uncertainty induced by the nonnegligible noise in the estimation process. This paper proposes a sensorless scheme to estimate the unknown contact force induced by the physical interaction with robots. The model-based identification scheme is initially used to obtain dynamic parameters. Then, neural learning of friction approximation is designed to enhance estimation performance for robotic systems subject with the model uncertainty. The external force exerted on the robot is estimated by a disturbance observer which models the external disturbance. A momentum observer is modified to develop a disturbance Kalman filter-based approach for estimating the contact force. The neural network-based model uncertainty and measurement noise level are analysed to guarantee the robustness of the Kalman filter-based force observer. The proposed scheme is verified by the measurement data from a heavy-duty industrial robot with 6 degrees of freedom (KUKA AUGLIS six). The experimental results are used to demonstrate the estimation performance of the proposed approach by the comparison with the existing schemes.

  • 28.
    Liu, Yongkui
    et al.
    Xidian Univ, Sch Mechanoelect Engn, Xian 710071, Peoples R China..
    Liang, Huagang
    Changan Univ, Sch Elect & Control, Xian 710064, Peoples R China..
    Xiao, Yingying
    Beijing Inst Elect Syst Engn, State Key Lab Complex Prod Intelligent Mfg Syst T, Beijing 100854, Peoples R China..
    Zhang, Haifeng
    Changan Univ, Sch Elect & Control, Xian 710064, Peoples R China..
    Zhang, Jingxin
    Changan Univ, Sch Elect & Control, Xian 710064, Peoples R China..
    Zhang, Lin
    Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Logistics-involved service composition in a dynamic cloud manufacturing environment: A DDPG-based approach2022In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 76, p. 102323-, article id 102323Article in journal (Refereed)
    Abstract [en]

    Service composition as an important technique for combining multiple services to construct a value-added service is a major research issue in cloud manufacturing. Highly dynamic environments present great challenges to cloud manufacturing service composition (CMfg-SC). Most of previous studies employ heuristic algorithms to solve service composition issues in cloud manufacturing, which, however, are designed for specific problems and lack adaptability necessary to dynamic environment. Hence, CMfg-SC calls for new adaptive approaches. Recent advances in deep reinforcement learning (DRL) provide a new means for solving this issue. Based on DRL, we propose a Deep Deterministic Policy Gradient (DDPG)-based service composition approach to cloud manufacturing, with which optimal service composition solutions can be learned through repeated training. Performance of DDPG in solving CMfg-SC in both static and dynamic environments is examined. Results obtained with another DRL algorithm -Deep Q-Networks (DQN) and the traditional Ant Colony Optimization (ACO) are also presented. Comparison indicates that DDPG has better adaptability, robustness, and extensibility to dynamic environments than ACO, although ACO converges faster and its steady QoS value of the service composition solution is higher than that of DDPG by 0.997%. DDPG outperforms DQN in convergence speed and stability, and the QoS value of the service composition solution of DDPG is higher than that of DQN by 3.249%.

  • 29.
    Liu, Yongkui
    et al.
    Xidian Univ, Sch Mechano Elect Engn, Xian 710071, Shaanxi, Peoples R China..
    Ping, Yaoyao
    Xidian Univ, Sch Mechano Elect Engn, Xian 710071, Shaanxi, Peoples R China..
    Zhang, Lin
    Beihang Univ, Sch Automation Sci & Elect Engn, Beijing 100191, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Xu, Xun
    Univ Auckland, Dept Mech Engn, Auckland 1142, New Zealand..
    Scheduling of decentralized robot services in cloud manufacturing with deep reinforcement learning2023In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 80, p. 102454-, article id 102454Article in journal (Refereed)
    Abstract [en]

    Cloud manufacturing is a service-oriented manufacturing model that offers manufacturing resources as cloud services. Robots are an important type of manufacturing resources. In cloud manufacturng, large-scale distrib-uted robots are encapsulated into cloud services and provided to consumers in an on-demand manner. How to effectively and efficiently manage and schedule decentralized robot services in cloud manufacturing to achieve on-demand provisioning is a challenging issue. During the past few years, Deep Reinforcement Learning (DRL) has become very popular and successfully been applied to many different areas such as games, robotics, and manufacturing. DRL also holds tremendous potential for solving scheduling issues in cloud manufacturing. To this end, this paper is devoted to exploring effective approaches for scheduling of decentralized robot manufacturing services in cloud manufacturing with DRL. Specifically, both Deep Q-Networks (DQN) and Dueling Deep Q-Networks (DDQN)-based scheduling algorithms are proposed. Performance of different algo-rithms, including DQN, DDQN, and other three benchmark algorithms, indicates that DDQN performs the best with respect to each indicator. Effects of different combinations of weight coefficients and influencing degrees of different indicators on the overall scheduling objective are analyzed. Results indicate that the DDQN-based scheduling algorithm is able to generate scheduling solutions efficiently.

  • 30.
    Liu, Yongkui
    et al.
    Xidian Univ, Xian, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems. KTH Royal Inst Technol, Stockholm, Sweden..
    Xu, Xun
    Univ Auckland, Auckland, New Zealand..
    Zhang, Lin
    Beihang Univ, Beijing, Peoples R China..
    Wang, Xi Vincent
    KTH, Centres, XPRES, Excellence in production research. KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Industrial Internet for Manufacturing2021In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 70, article id 102135Article in journal (Other academic)
  • 31.
    Liu, Yongkui
    et al.
    Xidian Univ, Sch Mechanoelect Engn, Xi'an 710071, Peoples R China..
    Xu, He
    Xidian Univ, Sch Mechanoelect Engn, Xi'an 710071, Peoples R China..
    Liu, Ding
    Xidian Univ, Sch Mechanoelect Engn, Xi'an 710071, Peoples R China.;Xidian Univ, Hangzhou Inst Technol, Intelligent Robot Lab, Hangzhou, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    A digital twin-based sim-to-real transfer for deep reinforcement learning-enabled industrial robot grasping2022In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 78, p. 102365-, article id 102365Article in journal (Refereed)
    Abstract [en]

    Deep reinforcement learning (DRL) has proven to be an effective framework for solving various complex control problems. In manufacturing, industrial robots can be trained to learn dexterous manipulation skills from raw pixels with DRL. However, training robots in the real world is a time-consuming, high-cost and of safety concerns process. A frequently adopted approach for easing this is to train robots through simulations first and then deploy algorithms (or policies) on physical robots. How to transfer policies of robot learning from simulation to the real world is a challenging issue. Digital twin that is able to create a dynamic, up-to-date representation of a physical robotic grasping system provides an effective approach for addressing this issue. In this paper, we focus on the scenario of DRL-based assembly-oriented industrial grasping and propose a digital twin-enabled approach for achieving effective transfer of DRL algorithms to a physical robot. Two parallel training systems, i.e., the physical robotic system and corresponding digital twin system, respectively, are established, which take virtual and real images as inputs. The output of the digital twin system is used to correct the real grasping point so that accurate grasping can be achieved. Experimental results verify the effectiveness of the intelligent grasping algorithm and the digital twin-enabled sim-to-real transfer approach and mechanism.

  • 32.
    Liu, Zhihao
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Wuhan Univ Technol, Sch Informat Engn,.
    Liu, Quan
    Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Peoples R China.;Wuhan Univ Technol, Hubei Key Lab Broadband Wireless Commun & Sensor, Wuhan 430070, Peoples R China..
    Xu, Wenjun
    Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Peoples R China.;Wuhan Univ Technol, Hubei Key Lab Broadband Wireless Commun & Sensor, Wuhan 430070, Peoples R China..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Zhou, Zude
    Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Peoples R China..
    Robot learning towards smart robotic manufacturing: A review2022In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 77, p. 102360-, article id 102360Article, review/survey (Refereed)
    Abstract [en]

    Robotic equipment has been playing a central role since the proposal of smart manufacturing. Since the beginning of the first integration of industrial robots into production lines, industrial robots have enhanced productivity and relieved humans from heavy workloads significantly. Towards the next generation of manufacturing, this review first introduces the comprehensive background of smart robotic manufacturing within robotics, machine learning, and robot learning. Definitions and categories of robot learning are summarised. Concretely, imitation learning, policy gradient learning, value function learning, actor-critic learning, and model-based learning as the leading technologies in robot learning are reviewed. Training tools, benchmarks, and comparisons amongst different robot learning methods are delivered. Typical industrial applications in robotic grasping, assembly, process control, and industrial human-robot collaboration are listed and discussed. Finally, open problems and future research directions are summarised.

  • 33.
    Lu, Yuqian
    et al.
    Univ Auckland, Dept Mech & Mechatron Engn, 20 Symonds St, Auckland 1010, New Zealand..
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Sustainable Production Systems.
    Bao, Jinsong
    Donghua Univ, Coll Mech Engn, Renming North Rd 2999, Shanghai 201620, Peoples R China..
    Lastra, Jose Martinez
    Tampere Univ, Fac Engn & Nat Sci, POB 600, FI-334100 Tampere, Finland..
    Ameri, Farhad
    Texas State Univ, Dept Engn Technol, 601 Univ Dr, San Marcos, TX 78666 USA..
    Semantic artificial intelligence for smart manufacturing automation2022In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 77, article id 102333Article in journal (Other academic)
  • 34.
    Lundholm, Thomas
    et al.
    KTH, Superseded Departments (pre-2005), Production Engineering.
    Bergström, Erik
    KTH, Superseded Departments (pre-2005), Production Engineering.
    Enarson, Daniel
    Harder, Lars
    KTH, Superseded Departments (pre-2005), Production Engineering.
    Lindström, Bo
    KTH, Superseded Departments (pre-2005), Production Engineering.
    Nicolescu, Mihai
    KTH, Superseded Departments (pre-2005), Production Engineering.
    Nilsson, Bruno
    KTH, Superseded Departments (pre-2005), Production Engineering.
    NEW TECHNIQUES APPLIED TO ADAPTIVE CONTROLLED MACHINING1992In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 9, no 4-5, p. 383-389Article in journal (Refereed)
    Abstract [en]

    The future capital intensive CIM and FMS systems will demand adaptive controlled (AC) machine tools. At the Department of Production Engineering of the Royal Institute of Technology in Stockholm (IMT/KTH) we are continuing the development of an advanced AC turning center. Our approach is to design and use sophisticated sensor systems to measure several features both on-line and off-line in order to obtain sufficient information on the cutting process and make adaptive feedback feasible. The AC system operates at three different levels: advanced process monitoring adaptive control constraint (ACC) adaptive control optimization (ACO). In this paper we give an overview of practical progress and improvements that have been achieved since our contribution to MSTF '87 in Cambridge.1 This includes: a new flexible sensor installation for optical tool wear measurements on-line tool wear estimation based upon a dynamic force sensor applied time series analysis for on-line chatter control real-time control of maching conditions with respect to cutting forces distributed real-time computer system solution.

  • 35.
    Lundholm, Thomas
    et al.
    KTH, Superseded Departments (pre-2005), Production Engineering.
    Yngen, Magnus
    KTH, Superseded Departments (pre-2005), Production Engineering.
    Lindström, Bo
    KTH, Superseded Departments (pre-2005), Production Engineering.
    Advanced Process Monitoring - A Major Step Towards Adaptive-Control1988In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 4, no 3-4, p. 413-421Article in journal (Refereed)
    Abstract [en]

    Adaptive Control (AC) of machine tools requires many kinds of measured input data. The more information about the complex metal cutting process that can be obtained, the better the process can be controlled.

    The paper describes an Adaptive Control Optimization (ACO) system for turning operations. The system continuously chooses Optimal Cutting Data (OCD), taking into account both economical criteria and technical limitations.

    The system operates at three different levels:

    • • Advanced Process Monitoring

    • • Adaptive Control Constraint (ACC)

    • • Adaptive Control Optimization (ACO).

    Two commercial monitoring systems perform process monitoring. In addition, five independent measurement systems have been developed.

    A dedicated vision system has been installed in the lathe to measure the tool flank wear between cuts. The flank wear data are utilized to predict the tool life. Based upon these predictions economical optimum cutting data can be calculated at the ACO level.

    To obtain in-process real-time control of the metal cutting process the cutting forces are measured during machining. The forces are measured with conventional piezoelectric force transducers which are located between the turret housing and the cross-slide. The measured force signals are processed by a dedicated microcontroller at the ACC level and cutting data adjustments are fed back to the machine control.

    A vibration measurement system, which either can be connected to an accelerometer or use the dynamic force signal from the piezoelectric force transducer, is part of a vibration control module at the ACC level. An ultra-fast signal processor performs the signal analysis.

    The remaining two measurement systems—a high frequency tool signal analysis system and a power spectra analysis system—are mentioned in the paper but not further discussed.

    Finally, the paper deals with how the strategies at the three different levels will be combined, in order to form an AC system. The monitoring tasks will always reside in the background and be activated if any failure occurs. The ACO subsystem will act as a path-finder and suggest cutting data. The active control tasks will, however, be carried out at the ACC level.

  • 36.
    Mo, Fan
    et al.
    Univ Nottingham, Inst Adv Mfg, Nottingham NG8 1BB, Notts, England..
    Rehman, Hamood Ur
    Univ Nottingham, Inst Adv Mfg, Nottingham NG8 1BB, Notts, England.;TQC Automation Ltd, Nottingham NG3 2NJ, Notts, England..
    Monetti, Fabio Marco
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Chaplin, Jack C.
    Univ Nottingham, Inst Adv Mfg, Nottingham NG8 1BB, Notts, England..
    Sanderson, David
    Univ Nottingham, Inst Adv Mfg, Nottingham NG8 1BB, Notts, England..
    Popov, Atanas
    Univ Nottingham, Inst Adv Mfg, Nottingham NG8 1BB, Notts, England..
    Maffei, Antonio
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Ratchev, Svetan
    Univ Nottingham, Inst Adv Mfg, Nottingham NG8 1BB, Notts, England..
    A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence2023In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 82, p. 102524-, article id 102524Article in journal (Refereed)
    Abstract [en]

    Digital twins and artificial intelligence have shown promise for improving the robustness, responsiveness, and productivity of industrial systems. However, traditional digital twin approaches are often only employed to augment single, static systems to optimise a particular process. This article presents a paradigm for combining digital twins and modular artificial intelligence algorithms to dynamically reconfigure manufacturing systems, including the layout, process parameters, and operation times of numerous assets to allow system decision -making in response to changing customer or market needs. A knowledge graph has been used as the enabler for this system-level decision-making. A simulation environment has been constructed to replicate the manufacturing process, with the example here of an industrial robotic manufacturing cell. The simulation environment is connected to a data pipeline and an application programming interface to assist the integration of multiple artificial intelligence methods. These methods are used to improve system decision-making and optimise the configuration of a manufacturing system to maximise user-selectable key performance indicators. In contrast to previous research, this framework incorporates artificial intelligence for decision -making and production line optimisation to provide a framework that can be used for a wide variety of manufacturing applications. The framework has been applied and validated in a real use case, with the automatic reconfiguration resulting in a process time improvement of approximately 10%.

  • 37. Newman, S.T.
    et al.
    Nassehi, A.
    Xu, Xun
    Rosso Jr., R.S.U.
    Wang, Lihui
    Integrated Manufacturing Technologies Institute, NRC, London, Ontario, Canada.
    Yusof, Y.
    Ali, L.
    Liu, R.
    Zheng, L.
    Kumar, S.
    Vichare, P.
    Dhokia, V.
    Strategic Advantages of Interoperability for Global Manufacturing Using CNC Technology2008In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 24, no 6, p. 699-708Article in journal (Refereed)
    Abstract [en]

    In the domain of manufacturing, computer numerically controllers (CNC) technology is a major contributor to the production capacity of the enterprises. The advances in CNC technology coupled with enhancements ill computing systems have provided the basis to re-examine the way in which computer-aided systems (CAx) call be used to enable global manufacturing. Interoperability of the various components of the CAx chain is therefore a major prerequisite for manufacturing enterprises for becoming strategically agile and consequently globally competitive. Being interoperable, resources call be utilized interchangeably in a plug-and-produce manner. Over the last 8 years the eminence of a STEP standard for machining entitled STEP-NC (numerical control) has become a well-known vehicle for research to improve the level of information availability at the CNC machine too]. Ill this paper, the authors introduce the background to the evolution of CNC manufacturing over the last 50 years and the Current standards available for programming. A review of the literature in interoperable CNC Manufacturing is then provided relating to Milling, turn-mill and other NC processes. The major part of the paper provides a strategic view of]low interoperability call be implemented across the CAx chain with a range of standards used to regulate the flow of information. Finally, the paper Outlines the advantages and major issues for future developments in interoperability, identifying future key requirements and limiting factors.

  • 38.
    Onori, Mauro
    et al.
    KTH, School of Industrial Engineering and Management (ITM).
    Gröndahl, P
    KTH, School of Industrial Engineering and Management (ITM).
    Langbeck, B
    KTH, School of Industrial Engineering and Management (ITM).
    The Mark III Flexible Automatic Assembly Cell1997In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537Article in journal (Refereed)
  • 39. Schmidt, Bernard
    et al.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Automatic work objects calibration via a global-local camera system2014In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 30, no 6, p. 678-683Article in journal (Refereed)
    Abstract [en]

    In a human robot collaborative manufacturing application where a work object can be placed in an arbitrary position, there is a need to calibrate the actual position of the work object. This paper presents an approach for automatic work-object calibration in flexible robotic systems. The approach consists of two modules: a global positioning module based on fixed cameras mounted around robotic workspace, and a local positioning module based on the camera mounted on the robot arm. The aim of the global positioning is to detect the work object in the working area and roughly estimate its position, whereas the local positioning is to define an object frame according to the 3D position and orientation of the work object with higher accuracy. For object detection and localization, coded visual markers are utilized. For each object, several markers are used to increase the robustness and accuracy of the localization and calibration procedure. This approach can be used in robotic welding or assembly applications.

  • 40.
    Song, Yimin
    et al.
    Tianjin Univeristy.
    Gao, Hao
    Sun, Tao
    Tianjin University.
    Dong, Gang
    Lian, Binbin
    Key Laboratory of Mechanism Theory and Equipment Design, Ministry of Education, Tianjin University, Tianjin 300072, China.
    Kinematic analysisand optimal design of a novel 1T3R parallel manipulator with an articulated travelling plate2014In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 30, no 5, p. 508-516Article in journal (Refereed)
    Abstract [en]

    Driven by the requirements of the large-scale component assemblage for the docking platform, this paper proposes a novel one-translational-three-rotational (1T3R) parallel manipulator with an articulated travelling plate, which can provide high stiffness and good accuracy performances in the assemblage. The underlying architecture of this manipulator is briefly addressed with emphasis on the practical realization of the articulated travelling plate. On the basis of the kinematic analysis of the 1T3R parallel manipulator, its optimal design considering the force and motion transmissibility is carried out, in which the generalized virtual power transmissibility of this manipulator is defined. This paper aims at laying a solid theoretical and technical foundation for the prototype design and manufacture of the 1T3R parallel manipulator.

  • 41.
    Sun, Tao
    et al.
    Tianjin University.
    Song, Yimin
    Tianjin Univeristy.
    Dong, Gang
    Lian, Binbin
    School of Mechanical Engineering, Tianjin University, Tianjin 300072, China.
    Liu, Jianping
    Optimal design of a parallel mechanism with three rotational degrees of freedom2012In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 28, no 4, p. 500-508Article in journal (Refereed)
    Abstract [en]

    This paper presents the concept design of a pose-adjustment system applied in the large fuselage or wing assembly of aircraft manufacturing which including a 3-degree-of-freedom rotational parallel mechanism (3-DoFs RPM), pogo columns and three tracks. The optimal design of the 3-DoFs RPM with its topology a 3-PUS&S mechanism is detailed, which is designed as a rigid yet compact module that can act as a pose-adjustment mechanism moving along three long tracks for large aircraft structural component assembly, a middle fuselage for example. Inverse kinematics of the 3-DoFs RPM with the exponential product method is achieved to lay the foundation for its kinematic synthesis. Next, with the commercial mathematical software, one can get the reachable workspace and define the prescribed workspace, respectively. Then, dimensional synthesis of the 3-DoFs RPM is executed to achieve a relatively good kinematic performance within its workspace. With the commercial CAE software, stiffness analysis is carried out for performance evaluation of the 3-DoFs RPM virtual prototype.

  • 42.
    Theissen, Nikolas Alexander
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Manufacturing and Metrology Systems.
    Laspas, Theodoros
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Manufacturing and Metrology Systems.
    Archenti, Andreas
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Manufacturing and Metrology Systems.
    Closed-force-loop elastostatic calibration of serial articulated robots2019In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 57, p. 86-91Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel methodology to measure the compliance of articulated serial robots based on the Elastically Linked Systems concept. The idea behind the methodology is to measure serial articulated robots with customized external wrench vectors under a closed-force-loop. The methodology proposes to measure robots in use-case defined configurations to increase the effect of the identified model parameters on their later implementation. The measurement methodology utilizes the Loaded Double Ball Bar to customize wrench vectors and a laser tracker to measure the system response. In particular, the Loaded Double Ball Bar creates the closed-force-loop to create a flow of forces similar to the intended application of the robot. The methodology is applied to an industrial robot with six rotary joints using the LDBB and a laser tracker. Finally, the paper ends on a discussion about the implementation of the model parameters to improve the accuracy of robots as well as challenges to realize a more cost efficient elastostatic calibration.

  • 43.
    Wang, Baicun
    et al.
    State Key Lab of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China; bKey Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
    Zhou, Huiying
    State Key Lab of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
    Li, Xingyu
    School of Engineering Technology, Purdue University, West Lafayette, United States.
    Yang, Geng
    State Key Lab of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China; dZhejiang Key Laboratory of Intelligent Operation and Maintenance Robot, Hangzhou, China.
    Zheng, Pai
    Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Regions, China, Hong Kong Special Administrative Regions.
    Song, Ci
    State Key Lab of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
    Yuan, Yixiu
    State Key Lab of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
    Wuest, Thorsten
    Department of Industrial and Management Systems Engineering, West Virginia University, Morgantown, WV 26506, United States.
    Yang, Huayong
    State Key Lab of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Human Digital Twin in the context of Industry 5.02024In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 85, article id 102626Article, review/survey (Refereed)
    Abstract [en]

    Human-centricity, a core value of Industry 5.0, places humans in the center of production. It leads to the prioritization of human needs, spanning from health and safety to self-actualization and personal growth. The concept of the Human Digital Twin (HDT) is proposed as a critical method to realize human-centricity in smart manufacturing systems towards Industry 5.0. HDTs are digital representations of humans, aiming to change the practice of human-system integration by coupling humans’ characteristics directly to the system design and its performance. In-depth analysis, critical insights, and application guidelines of HDT are essential to realize the concept of Industry 5.0 in practice and evolve the smart manufacturing paradigm in modern factories. However, the investigation on the development of HDT to evolve humans’ roles and develop humans to their full potential is limited to date. Recent studies are rarely geared towards designing a standardized framework and architecture of HDT for diverse real-world applications. Thus, this work aims to close this research gap by carrying out a comprehensive survey on HDT in the context of Industry 5.0, summarizing the ongoing evolution, and proposing a proper connotation of HDT, before discussing the conceptual framework and system architecture of HDT and analyzing enabling technologies and industrial applications. This work provides guidance on possible avenues as well as challenges for the further development of HDT and its related concepts, allowing humans to reach their potential and accommodating their diverse needs in the futuristic smart manufacturing systems shaped by Industry 5.0.

  • 44.
    Wang, Lihui
    National Research Council of Canada, Integrated Manufacturing Technologies Institute.
    Integrated Design-to-Control Approach for Holonic Manufacturing Systems2001In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 8, no 1, p. 81-93Article in journal (Refereed)
    Abstract [en]

    Next generation manufacturing systems will be integrated networks of distributed resources simultaneously capable of combined knowledge and material processing. These manufacturing systems will be required to be agile, flexible, and fault-tolerant. The objective of this research is to define a generic open architecture for such kind of distributed manufacturing systems, especially for holonic manufacturing systems (HMS). This paper will address issues associated with HMS, and propose a reference architecture based on a design-to-control concept. The primary focus will be given to the collaborative and integrated design-to-control approach based on machining feature, agent technology, and function block standards. Emphasis is also extended and given to metamorphic process planning and control of HMS using multi-agent negotiation and cooperation. The proposed approach, together with the open architecture, shows much promise for improving the entire manufacturing system performance under the ever-changing real-time and distributed shop floor environments.

  • 45.
    Wang, Lihui
    et al.
    National Research Council of Canada.
    Orban, Peter
    Cunningham, Andrew
    Lang, Sherman
    Remote Real-Time CNC Machining for Web-Based Manufacturing2004In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 20, no 4, p. 563-571Article in journal (Refereed)
    Download full text (pdf)
    fulltext
  • 46.
    Wang, Lihui
    et al.
    Integrated Manufacturing Technologies Institute, National Research Council of Canada.
    Sams, R.
    Verner, M.
    Xi, F.
    Integrating Java 3D Model and Sensor Data for Remote Monitoring and Control2003In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 19, no 1-2, p. 13-19Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel approach and a framework for web-based systems that can be used in distributed manufacturing environments. A prototype is developed to demonstrate its application to remote monitoring and control of a Tripod - one type of parallel kinematic machine. It utilizes the latest Java technologies (Java 3D and Java Servlets) as enabling technologies for system implementation. Instead of using a camera for monitoring, the Tripod is modeled using Java 3D with behavioral control nodes embedded. Once downloaded from its server, the 3D model behaves in the same way of its counterpart at client side. It remains alive by connecting with the Tripod through message passing, e.g., sensor signals and control commands transmissions. The goal of this research is to eliminate network traffic with Java 3D models, while still providing users with intuitive environments. In the near future, open-architecture devices will be web-ready having Java virtual machines embedded. This will make the approach more effective for web-based device monitoring and control.

  • 47. Wang, Lihui
    et al.
    Xi, F.
    FAIM 20042005In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 21, no 4-5Article in journal (Refereed)
  • 48.
    Wang, Lihui
    et al.
    National Research Council of Canada .
    Xi, F.
    FAIM 20142005In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 21, no 4-5, p. 389-390Article in journal (Refereed)
  • 49.
    Wang, Lihui
    et al.
    Integrated Manufacturing Technologies Institute, National Research Council of Canada.
    Xi, Fengfeng
    Zhang, Dan
    A parallel robotic attachment and its remote manipulation2006In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 22, no 5-6, p. 515-525Article in journal (Refereed)
    Abstract [en]

    This paper discusses a 3-dof (degree of freedom) parallel robotic attachment and its remote manipulation. This attachment is designed as a tripod that provides two rotary motions and one linear motion. The attachment can be mounted onto a variety of machines for different applications, including CNC milling machines, industrial robots, and CMM. Java technologies are used to develop a remote manipulation system for the parallel robotic attachment, including remote monitoring and control. The main difference of this system from the existing web-based or internet-based remote systems is the way to control the motion of the machine from a remote site. Instead of using a camera for monitoring, the tripod is modeled using 3D computer graphics with behavioral control nodes embedded.

    Compared with camera-based solutions, network traffic is largely reduced, thereby making real-time remote device manipulation practical on the web. Our parallel robotic attachment is one type of parallel kinematic mechanisms (PKM). With PKM emerging as a new way of building flexible systems or agile machines, its advantage over serial mechanism is also presented.

    Download full text (pdf)
    fulltext
  • 50.
    Wang, Tianyu
    et al.
    UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai, cy=China.
    Liu, Zhihao
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Li, Mian
    Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, cy=China.
    Wang, Xi Vincent
    KTH, School of Industrial Engineering and Management (ITM), Production engineering.
    Data-efficient multimodal human action recognition for proactive human–robot collaborative assembly: A cross-domain few-shot learning approach2024In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 89, article id 102785Article in journal (Refereed)
    Abstract [en]

    With the recent vision of Industry 5.0, the cognitive capability of robots plays a crucial role in advancing proactive human–robot collaborative assembly. As a basis of the mutual empathy, the understanding of a human operator's intention has been primarily studied through the technique of human action recognition. Existing deep learning-based methods demonstrate remarkable efficacy in handling information-rich data such as physiological measurements and videos, where the latter category represents a more natural perception input. However, deploying these methods in new unseen assembly scenarios requires first collecting abundant case-specific data. This leads to significant manual effort and poor flexibility. To deal with the issue, this paper proposes a novel cross-domain few-shot learning method for data-efficient multimodal human action recognition. A hierarchical data fusion mechanism is designed to jointly leverage the skeletons, RGB images and depth maps with complementary information. Then a temporal CrossTransformer is developed to enable the action recognition with very limited amount of data. Lightweight domain adapters are integrated to further improve the generalization with fast finetuning. Extensive experiments on a real car engine assembly case show the superior performance of proposed method over state-of-the-art regarding both accuracy and finetuning efficiency. Real-time demonstrations and ablation study further indicate the potential of early recognition, which is beneficial for the robot procedures generation in practical applications. In summary, this paper contributes to the rarely explored realm of data-efficient human action recognition for proactive human–robot collaboration.

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