With advancements in Information and Communication Technologies (ICT), traditional manufacturing industries are engaged in a digital transformation. This transformation enables the acquisition of vast amounts of data and information, enhancing decision-making capabilities. This, in turn, has raised the expectations of field operators who seek data and information management tailored to the dynamic working environment, thereby improving efficiency in their daily operations. However, there is a lack of a holistic approach to integrating diverse data sources, extracting valuable contextual information, and delivering real-time information to field operators. This paper addresses this gap by proposing an adaptive, interoperable, and user-centered Context-Aware System (CAS). Initially, the paper explores the challenges and requirements associated with CAS’s current practices while proposing potential solutions. Furthermore, it introduces a system framework of CAS that integrates Digital Twin (DT) and semantic technologies. This framework includes three primary technical solutions: (1) Integrating DT to create a comprehensive digital representation of physical entities, enabling real-time data integration and synchronization; (2) Providing an ontology-based approach to model manufacturing context, facilitating knowledge representation and reasoning; (3) Developing a user-centered information delivery system leveraging Augmented Reality (AR) for context-aware visualization. The system architecture has been implemented and tested in a laboratory-scale industrial environment, focusing on crane operations within logistics scenarios. Lastly, three use cases are presented to demonstrate the system’s practical applicability, showcasing its feasibility in furnishing informed contextual information to end-users within the dynamic manufacturing environment.

Combining machine learning and formal methods (FMs) provides a possible solution to overcome the safety issue of autonomous driving (AD) vehicles. However, there are gaps to be bridged before this combination becomes practically applicable and useful. In an attempt to facilitate researchers in both FMs and AD areas, this paper proposes a framework that combines two well-known tools, namely CommonRoad and UPPAAL. On the one hand, CommonRoad can be enhanced by the rigorous semantics of models in UPPAAL, which enables a systematic and comprehensive understanding of the AD system’s behaviour and thus strengthens the safety of the system. On the other hand, controllers synthesised by UPPAAL can be visualised by CommonRoad in real-world road networks, which facilitates AD vehicle designers greatly adopting formal models in system design. In this framework, we provide automatic model conversions between CommonRoad and UPPAAL. Therefore, users only need to program in Python and the framework takes care of the formal models, learning, and verification in the backend. We perform experiments to demonstrate the applicability of our framework in various AD scenarios, discuss the advantages of solving motion planning in our framework, and show the scalability limit and possible solutions.

Topography estimation is essential for autonomous off-road navigation. Common methods rely on point cloud data from, e.g., Light Detection and Ranging sensors (LIDARs) and stereo cameras. Stereo cameras produce dense point clouds with larger coverage but lower accuracy. LIDARs, on the other hand, have higher accuracy and longer range but much less coverage. LIDARs are also more expensive. The research question examines whether incorporating LIDARs can significantly improve stereo camera accuracy. Current sensor fusion methods use LIDARs' raw measurements directly; thus, the improvement in estimation accuracy is limited to only LIDAR-scanned locations The main contribution of our new method is to construct a reference ground plane through the interpolation of LIDAR data so that the interpolated maps have similar coverage as the stereo camera's point cloud. The interpolated maps are fused with the stereo camera point cloud via Kalman filters to improve a larger section of the topography map. The method is tested in three environments: controlled indoor, semi-controlled outdoor, and unstructured terrain. Compared to the existing method without LIDAR interpolation, the proposed approach reduces average error by 40% in the controlled environment and 67% in the semi-controlled environment, while maintaining large coverage. The unstructured environment evaluation confirms its corrective impact.

The growing adoption of deep learning, particularly supervised learning, in the manufacturing highlights the need for large labeled datasets. However, generating domain-specific labeled data is costly. Focusing on smart tightening diagnosis in manufacturing, prior research introduced the tightening diagnosis transformer (TDT), which leverages self-supervised transformers to reduce dependency on labeled data. Despite its advancements, TDT has two key limitations: (1) reliance solely on torque sensor data, ignoring angle sensor data, and (2) added computational overhead from self-supervised learning, which is problematic in resource-limited shop-floor environments. This study presents a novel transformer-based multi-label classification method that integrates sensor fusion and reduces needs for both computation and labeled data. We enhance the state-of-the-art TDT by introducing the angle positional encoder (APE), enabling feature-level sensor fusion for supervised learning. Additionally, we propose a self-supervised learning method for APE-enhanced TDT to reduce the need for extensive labeled datasets. We also introduce the random sequence patchifier (RSP), a transformer-specific data augmentation technique that improves generalization and reduces computational cost. Finally, we adopt annealing augmentation scheduling to mitigate the risk of learning “fake” feature representations (unrealistic artifacts created by the augmentations). Compared with previous TDT, our experiment evaluation demonstrates that the these introduced techniques improve Subset Accuracy and F1 scores by 10% and 7%. Moreover, the RSP-based augmentation reduces the training time by 12% for supervised learning and 15% for self-supervised learning.

In this chapter, we explore how new technologies and requirements affect current design methodologies for mechatronics. We investigate gaps and directions needed for the methodologies of tomorrow in view of trends affecting mechatronics and current state of the art. To fully reap the opportunities of mechatronics with advances in materials, sensors, additive manufacturing, AI, computing and communication, but also to handle new requirements and regulations, there is a need for new methodologies and architectures. We introduce the concept of “MechaOps” and related considerations that promise to assist in enhancing scalability, smartness, performance and sustainability for extended mechatronic products that collaborate with a smart infrastructure, humans and other mechatronic systems. MechaOps refers to the integration of the concepts of Mechatronics and DevOps. As opposed to DevOps in software engineering, MechaOps encompasses data gathering, upgrades/downgrades as well as reconfigurations considering both mechanics and/or software in a mechatronic product. With the life-cycle view implied by the MechaOps concept, it becomes essential to design for upgrading, downgrading, maintenance, reuse and refurbishment. The development of new methodologies requires overcoming disciplinary gaps, with specific considerations of novel architectures including digital twins, interactions with humans, other systems and a smart infrastructure, the role of AI in mechatronics, and in assuring trustworthiness and sustainability. We believe that new methodologies and architectures will initially be especially relevant for high-end systems, supporting the creation of adaptable and flexible mechatronics products and services with improved performance and reduced environmental footprint.

The architectural design of the electric vehicle (EV) powertrains profoundly impacts the EV’s cost, energy efficiency, range, and other properties. Integrated and optimization-based systems engineering is critical for the early design phase of EVs; however, conventional early-phase design methods are primarily subjective and qualitative. The optimal designs on structures, component sizes, efficient speed trajectories, and energy management control are performed either heuristically or separately. This paper presents a model-based systems engineering (MBSE) methodology for evaluating and optimizing EV powertrain architectures. The methodology contains three key contributions: (1) a formal MBSE toolbox that supports the design, code-generation, and co-optimization of EV architectures, (2) a simultaneous co-optimization method that integrates sizing, control, and eco-driving, and (3) a computationally efficient multi-objective optimization solution applicable for large-scale problems. The effectiveness and efficiency of this approach are demonstrated through the optimization and comparison of three distinct powertrain architectures. Compared with a reference EV, our co-optimization method reduces the energy cost by around 8% for highway driving conditions and around 13% for urban driving conditions. The component cost of the EV may also be reduced by around 10%. Compared with the efficient multi-objective NSGA-II algorithm, the proposed method obtains equivalent results with more than 90% time reduction.

Traditional supervisory control methods for the nonblocking control of discrete event systems often suffer from exponential computational complexity. Reinforcement learning-based approaches mitigate state explosion by sampling many random sequences instead of computing the synchronous product of multiple modular supervisors, but they struggle with limited reachable state spaces. A primary novelty of this study is to use the K-means clustering method for online inference with the learned state-action values. The clustering method divides all events at a state into the good group and the bad group. The events in the good group are allowed by the supervisor. The obtained supervisor policy can ensure both system constraints and larger control freedom compared to conventional RL-based supervisors. The proposed framework is validated by two case studies: an industrial transfer line (TL) system and an automated guided vehicle (AGV) system. In the TL case study, nonblocking reachable states increase from 56 to 72, while in the AGV case study, a substantial expansion from 481 to 3558 states is observed. Our new method achieves a balance between computational efficiency and nonblocking supervisory control.

This study presents an innovative approach to optimal gait control for a soft quadruped robot enabled by four compressible tendon-driven soft actuators. Soft quadruped robots, compared to their rigid counterparts, are widely recognized for offering enhanced safety, lower weight, and simpler fabrication and control mechanisms. However, their highly deformable structure introduces nonlinear dynamics, making precise gait locomotion control complex. To solve this problem, we propose a novel model-based reinforcement learning (MBRL) method. The study employs a multi-stage approach, including state space restriction, data-driven surrogate model training, and MBRL development. Compared to benchmark methods, the proposed approach significantly improves the efficiency and performance of gait control policies. The developed policy is both robust and adaptable to the robot's deformable morphology. The study concludes by highlighting the practical applicability of these findings in real-world scenarios.

This study focuses on the locomotion capability improvement in a tendon-driven soft quadruped robot through an online adaptive learning approach. Leveraging the inverse kinematics model of the soft quadruped robot, we employ a central pattern generator to design a parametric gait pattern, and use Bayesian optimization (BO) to find the optimal parameters. Further, to address the challenges of modeling discrepancies, we implement a multi-fidelity BO approach, combining data from both simulation and physical experiments throughout training and optimization. This strategy enables the adaptive refinement of the gait pattern and ensures a smooth transition from simulation to real-world deployment for the controller. Compared to previous result using a fixed gait pattern, the multi-fidelity BO approach improves the robot’s average walking speed from 0.14 m/s to 0.214 m/s, an increase of 52.7%. Moreover, we integrate a computational task off-loading architecture by edge computing, which reduces the onboard computational and memory overhead, to improve real-time control performance and facilitate an effective online learning process. The proposed approach successfully achieves optimal walking gait design for physical deployment with high efficiency, effectively addressing challenges related to the reality gap in soft robotics.

Four-dimensional (4D) printing is an advanced manufacturing technology that has rapidly emerged as a transformative tool with the capacity to reshape various research domains and industries. Distinguished by its integration of time as a dimension, 4D printing allows objects to dynamically respond to external stimuli, setting it apart from conventional 3D printing. This roadmap has been devised, by contributions of 44 active researchers in this field from 32 affiliations world-wide, to navigate the swiftly evolving landscape of 4D printing, consolidating recent advancements and making them accessible to experts across diverse fields, ranging from biomedicine to aerospace, textiles to electronics. The roadmap's goal is to empower both experts and enthusiasts, facilitating the exploitation of 4D printing's transformative potential to create intelligent, adaptive objects that are not only feasible but readily attainable. By addressing current and future challenges and proposing advancements in science and technology, it sets the stage for revolutionary progress in numerous industries, positioning 4D printing as a transformative tool for the future.