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  • 1.
    Antonova, Rika
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Kokic, Mia
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Stork, Johannes A.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation2018In: Proceedings of The 2nd Conference on Robot Learning, PMLR 87, 2018, p. 641-650Conference paper (Refereed)
    Abstract [en]

    We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.

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  • 2.
    Asif, Rizwan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Löffel, Hendrik Jan
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Assavasangthong, Vorapol
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Martinelli, Giulio
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Gajland, Phillip
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Theoretical Computer Science, TCS.
    Rodríguez Gálvez, Borja
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Aerial path planning for multi-vehicles2019In: Proceedings - IEEE 2nd International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 267-272, article id 8791733Conference paper (Refereed)
    Abstract [en]

    Unmanned Aerial Vehicles (UAV) are a potential solution to fast and cost efficient package delivery services. There are two types of UAVs, namely fixed wing (UAV-FW) and rotor wing (UAV-RW), which have their own advantages and drawbacks. In this paper we aim at providing different solutions to a collaborating multi-agent scenario combining both UAVs types. We show the problem can be reduced to the facility location problem (FLP) and propose two local search algorithms to solve it: Tabu search and simulated annealing.

  • 3.
    Brucker, Manuel
    et al.
    German Aerosp Ctr DLR, Inst Robot & Mechatron, D-82234 Oberpfaffenhofen, Germany..
    Durner, Maximilian
    German Aerosp Ctr DLR, Inst Robot & Mechatron, D-82234 Oberpfaffenhofen, Germany..
    Ambrus, Rares
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Marton, Zoltan Csaba
    German Aerosp Ctr DLR, Inst Robot & Mechatron, D-82234 Oberpfaffenhofen, Germany..
    Wendt, Axel
    Robert Bosch, Corp Res, St Joseph, MI USA.;Robert Bosch, Corp Res, Gerlingen, Germany..
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Arras, Kai O.
    Robert Bosch, Corp Res, St Joseph, MI USA.;Robert Bosch, Corp Res, Gerlingen, Germany..
    Triebel, Rudolph
    German Aerosp Ctr DLR, Inst Robot & Mechatron, D-82234 Oberpfaffenhofen, Germany.;Tech Univ Munich, Dep Comp Sci, Munich, Germany..
    Semantic Labeling of Indoor Environments from 3D RGB Maps2018In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE Computer Society, 2018, p. 1871-1878Conference paper (Refereed)
    Abstract [en]

    We present an approach to automatically assign semantic labels to rooms reconstructed from 3D RGB maps of apartments. Evidence for the room types is generated using state-of-the-art deep-learning techniques for scene classification and object detection based on automatically generated virtual RGB views, as well as from a geometric analysis of the map's 3D structure. The evidence is merged in a conditional random field, using statistics mined from different datasets of indoor environments. We evaluate our approach qualitatively and quantitatively and compare it to related methods.

  • 4.
    Båberg, Fredrik
    et al.
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Petter, Ögren
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Formation Obstacle Avoidance using RRT and Constraint Based Programming2017In: 2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR), IEEE conference proceedings, 2017, article id 8088131Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose a new way of doing formation obstacle avoidance using a combination of Constraint Based Programming (CBP) and Rapidly Exploring Random Trees (RRTs). RRT is used to select waypoint nodes, and CBP is used to move the formation between those nodes, reactively rotating and translating the formation to pass the obstacles on the way. Thus, the CBP includes constraints for both formation keeping and obstacle avoidance, while striving to move the formation towards the next waypoint. The proposed approach is compared to a pure RRT approach where the motion between the RRT waypoints is done following linear interpolation trajectories, which are less computationally expensive than the CBP ones. The results of a number of challenging simulations show that the proposed approach is more efficient for scenarios with high obstacle densities.

  • 5.
    Chen, Fei
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Second Order Consensus for Leader-follower Multi-agent Systems with Prescribed Performance2019In: IFAC PAPERSONLINE, ELSEVIER , 2019, Vol. 52, no 20, p. 103-108Conference paper (Refereed)
    Abstract [en]

    The problem of distributed control for second order leader-follower multi-agent systems with prescribed performance guarantees is investigated in this paper. Leader-follower is meant in the sense that a group of agents with external inputs are selected as leaders in order to drive the group of followers in a way that the entire system can achieve consensus within certain prescribed performance transient bounds. Under the assumption of tree graphs, we propose a distributed control law based on a backstepping approach for the group of leaders to steer the entire system achieving consensus within the prescribed performance bounds. Finally, a simulation example is given to verify the results. 

  • 6.
    Cruciani, Silvia
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH Royal Inst Technol, Div Robot Percept & Learning, EECS, S-11428 Stockholm, Sweden..
    Sundaralingam, Balakumar
    Univ Utah, Robot Ctr, Salt Lake City, UT 84112 USA.;Univ Utah, Sch Comp, Salt Lake City, UT 84112 USA..
    Hang, Kaiyu
    Yale Univ, Dept Mech Engn & Mat Sci, New Haven, CT 06520 USA..
    Kumar, Vikash
    Google AI, San Francisco, CA 94110 USA..
    Hermans, Tucker
    Univ Utah, Robot Ctr, Salt Lake City, UT 84112 USA.;Univ Utah, Sch Comp, Salt Lake City, UT 84112 USA.;NVIDIA Res, Santa Clara, CA USA..
    Kragic, Danica
    KTH, Superseded Departments (pre-2005), Numerical Analysis and Computer Science, NADA. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH Royal Inst Technol, Div Robot Percept & Learning, EECS, S-11428 Stockholm, Sweden..
    Benchmarking In-Hand Manipulation2020In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 5, no 2, p. 588-595Article in journal (Refereed)
    Abstract [en]

    The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-hand manipulation systems. The goal is to assess the systems ability to change the pose of a hand-held object by either using the fingers, environment or a combination of both. Given an object surface mesh from the YCB data-set, we provide examples of initial and goal states (i.e. static object poses and fingertip locations) for various in-hand manipulation tasks. We further propose metrics that measure the error in reaching the goal state from a specific initial state, which, when aggregated across all tasks, also serves as a measure of the systems in-hand manipulation capability. We provide supporting software, task examples, and evaluation results associated with the benchmark.

  • 7.
    Duberg, Daniel
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    The Obstacle-restriction Method for Tele-operation of Unmanned Aerial Vehicles with Restricted Motion2018In: 2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV), IEEE , 2018, p. 266-273Conference paper (Refereed)
    Abstract [en]

    This paper presents a collision avoidance method for tele-operated unmanned aerial vehicles (UAVs). The method is designed to assist the operator at all times, such that the operator can focus solely on the main objectives instead of avoiding obstacles. We restrict the altitude to be fixed in a three dimensional environment to simplify the control and operation of the UAV. The method contributes a number of desired properties not found in other collision avoidance systems for tele-operated UAVs. Our method i) can handle situations where there is no input from the user by actively stopping and proceeding to avoid obstacles, ii) allows the operator to slide between prioritizing staying away from objects and getting close to them in a safe way when so required, and iii) provides for intuitive control by not deviating too far from the control input of the operator. We demonstrate the effectiveness of the method in real world experiments with a physical hexacopter in different indoor scenarios. We also present simulation results where we compare controlling the UAV with and without our method activated.

  • 8.
    Filotheou, Alexandros
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Nikou, Alexandros
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Robust decentralised navigation of multi-agent systems with collision avoidance and connectivity maintenance using model predictive controllers2018In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820Article in journal (Other academic)
    Abstract [en]

    , with static obstacles. In particular, we propose a decentralised control protocol such that each agent reaches a predefined position at the workspace, while using local information based on a limited sensing radius. The proposed scheme guarantees that the initially connected agents remain always connected. In addition, by introducing certain distance constraints, we guarantee inter-agent collision avoidance as well as collision avoidance with the obstacles and the boundary of the workspace. The proposed controllers employ a class of Decentralized Nonlinear Model Predictive Controllers (DNMPC) under the presence of disturbances and uncertainties. Finally, simulation results verify the validity of the proposed framework.

  • 9. Guo, Meng
    et al.
    Bechlioulis, Charalampos P.
    Kyriakopoulos, Kostas J.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Hybrid Control of Multiagent Systems With Contingent Temporal Tasks and Prescribed Formation Constraints2017In: IEEE Transactions on Big Data, ISSN 2325-5870, E-ISSN 2168-6750, Vol. 4, no 4, p. 781-792Article in journal (Refereed)
    Abstract [en]

    In this paper, we present a distributed hybrid control strategy for multiagent systems with contingent temporal tasks and prescribed formation constraints. Each agent is assigned a local task given as a linear temporal logic formula. In addition, two commonly seen kinds of cooperative robotic tasks, namely, service and formation, are requested and exchanged among the agents in real time. The service request is a short-term task provided by one agent to another. On the other hand, the formation request is a relative deployment requirement with predefined transient response imposed by an associated performance function. The proposed hybrid control strategy consists of four major components: 1) the contingent requests handlingmodule; 2) the real-time events monitoring module; 3) the local discrete plan synthesis module; and 4) the continuous control switching module, and it is shown that all local tasks and contingent service/formation requests are fulfilled. Finally, a simulated paradigm demonstrates the proposed control strategy.

  • 10.
    Guo, Meng
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Boskos, Dimitris
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Tumova, Jana
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Distributed hybrid control synthesis for multi-agent systems from high-level specifications2018In: Control Subject to Computational and Communication Constraints, Springer Verlag , 2018, 475, p. 241-260Chapter in book (Refereed)
    Abstract [en]

    Current control applications necessitate in many cases the consideration of systems with multiple interconnected components. These components/agents may need to fulfill high-level tasks at a discrete planning layer and also coupled constraints at the continuous control layer. Toward this end, the need for combined decentralized control at the continuous layer and planning at the discrete layer becomes apparent. While there are approaches that handle the problem in a top-down centralized manner, decentralized bottom-up approaches have not been pursued to the same extent. We present here some of our results for the problem of combined, hybrid control and task planning from high-level specifications for multi-agent systems in a bottom-up manner. In the first part, we present some initial results on extending the necessary notion of abstractions to multi-agent systems in a distributed fashion. We then consider a setup where agents are assigned individual tasks in the form of linear temporal logic (LTL) formulas and derive local task planning strategies for each agent. In the last part, the problem of combined distributed task planning and control under coupled continuous constraints is further considered.

  • 11.
    Heshmati-Alamdari, Shahab
    et al.
    Natl Tech Univ Athens, Dept Mech Engn, Control Syst Lab, 9 Heroon Polytech St, Zografos 15780, Greece..
    Bechlioulis, Charalampos P.
    Natl Tech Univ Athens, Dept Mech Engn, Control Syst Lab, 9 Heroon Polytech St, Zografos 15780, Greece..
    Karras, George C.
    Natl Tech Univ Athens, Dept Mech Engn, Control Syst Lab, 9 Heroon Polytech St, Zografos 15780, Greece..
    Nikou, Alexandros
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Kyriakopoulos, Kostas J.
    Natl Tech Univ Athens, Dept Mech Engn, Control Syst Lab, 9 Heroon Polytech St, Zografos 15780, Greece..
    A robust interaction control approach for underwater vehicle manipulator systems2018In: Annual Reviews in Control, ISSN 1367-5788, E-ISSN 1872-9088, Vol. 46, p. 315-325Article, review/survey (Refereed)
    Abstract [en]

    In underwater robotic interaction tasks (e.g., sampling of sea organisms, underwater welding, panel handling, etc) various issues regarding the uncertainties and complexity of the robot dynamic model, the external disturbances (e.g., sea currents), the steady state performance as well as the overshooting/undershooting of the interaction force error, should be addressed during the control design. Motivated by the aforementioned considerations, this paper presents a force/position tracking control protocol for an Underwater Vehicle Manipulator System (UVMS) in compliant contact with a planar surface, without incorporating any knowledge of the UVMS dynamic model, the exogenous disturbances or the contact stiffness model. Moreover, the proposed control framework guarantees: (i) certain predefined minimum speed of response, maximum steady state error as well as overshoot/undershoot concerning the force/position tracking errors, (ii) contact maintenance and (iii) bounded closed loop signals. Additionally, the achieved transient and steady state performance is solely determined by certain designer-specified performance functions/parameters and is fully decoupled from the control gain selection and the initial conditions. Finally, both simulation and experimental studies clarify the proposed method and verify its efficiency.

  • 12.
    Kokic, Mia
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Bohg, Jeannette
    Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA..
    Learning Task-Oriented Grasping From Human Activity Datasets2020In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 5, no 2, p. 3352-3359Article in journal (Refereed)
    Abstract [en]

    We propose to leverage a real-world, human activity RGB dataset to teach a robot <italic>Task-Oriented Grasping</italic> (TOG). We develop a model that takes as input an RGB image and outputs a hand pose and configuration as well as an object pose and a shape. We follow the insight that jointly estimating hand and object poses increases accuracy compared to estimating these quantities independently of each other. Given the trained model, we process an RGB dataset to automatically obtain the data to train a TOG model. This model takes as input an object point cloud and outputs a suitable region for task-specific grasping. Our ablation study shows that training an object pose predictor with the hand pose information (and vice versa) is better than training without this information. Furthermore, our results on a real-world dataset show the applicability and competitiveness of our method over state-of-the-art. Experiments with a robot demonstrate that our method can allow a robot to preform TOG on novel objects.

  • 13.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Acting, Interacting, Collaborative Robots2017In: PROCEEDINGS OF THE 2017 ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION (HRI'17), IEEE , 2017, p. 293-293Conference paper (Refereed)
    Abstract [en]

    The current trend in computer vision is development of data-driven approaches where the use of large amounts of data tries to compensate for the complexity of the world captured by cameras. Are these approaches also viable solutions in robotics? Apart from 'seeing', a robot is capable of acting, thus purposively change what and how it sees the world around it. There is a need for an interplay between processes such as attention, segmentation, object detection, recognition and categorization in order to interact with the environment. In addition, the parameterization of these is inevitably guided by the task or the goal a robot is supposed to achieve. In this talk, I will present the current state of the art in the area of robot vision and discuss open problems in the area. I will also show how visual input can be integrated with proprioception, tactile and force-torque feedback in order to plan, guide and assess robot's action and interaction with the environment. Interaction between two agents builds on the ability to engage in mutual prediction and signaling. Thus, human-robot interaction requires a system that can interpret and make use of human signaling strategies in a social context. Our work in this area focuses on developing a framework for human motion prediction in the context of joint action in HRI. We base this framework on the idea that social interaction is highly influences by sensorimotor contingencies (SMCs). Instead of constructing explicit cognitive models, we rely on the interaction between actions the perceptual change that they induce in both the human and the robot. This approach allows us to employ a single model for motion prediction and goal inference and to seamlessly integrate the human actions into the environment and task context. We employ a deep generative model that makes inferences over future human motion trajectories given the intention of the human and the history as well as the task setting of the interaction. With help predictions drawn from the model, we can determine the most likely future motion trajectory and make inferences over intentions and objects of interest.

  • 14.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    From active perception to deep learning2018In: SCIENCE ROBOTICS, ISSN 2470-9476, Vol. 3, no 23, article id eaav1778Article in journal (Other academic)
  • 15.
    Lindemann, Lars
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Feedback control strategies for multi-agent systems under a fragment at) of signal temporal logic tasks2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 106, p. 284-293Article in journal (Refereed)
    Abstract [en]

    Multi-agent systems under temporal logic tasks have great potential due to their ability to deal with complex tasks. The control of these systems, however, poses many challenges and the majority of existing approaches result in large computational burdens. We instead propose computationally efficient and robust feedback control strategies for a class of systems that are, in a sense, feedback equivalent to single integrator systems, but where the dynamics are partially unknown for the control design. A bottom-up scenario is considered in which each agent is subject to a local task from a limited signal temporal logic fragment. Notably, the satisfaction of a local task may also depend on the behavior of other agents. We provide local continuous-time feedback control laws that, under some sufficient conditions, guarantee satisfaction of the local tasks. Otherwise, a local detection & repair scheme is proposed in combination with the previously derived feedback control laws to deal with infeasibilities, such as when local tasks are conflicting. The efficacy of the proposed method is demonstrated in simulations.

  • 16.
    Lindemann, Lars
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Robust control for signal temporal logic specifications using discrete average space robustness2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 101, p. 377-387Article in journal (Refereed)
    Abstract [en]

    Control systems that satisfy temporal logic specifications have become increasingly popular due to their applicability to robotic systems. Existing control methods, however, are computationally demanding, especially when the problem size becomes too large. In this paper, a robust and computationally efficient model predictive control framework for signal temporal logic specifications is proposed. We introduce discrete average space robustness, a novel quantitative semantic for signal temporal logic, that is directly incorporated into the cost function of the model predictive controller. The optimization problem entailed in this framework can be written as a convex quadratic program when no disjunctions are considered and results in a robust satisfaction of the specification. Furthermore, we define the predicate robustness degree as a new robustness notion. Simulations of a multi-agent system subject to complex specifications demonstrate the efficacy of the proposed method.

  • 17. Linsenmayer, Steffen
    et al.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Allgoewer, Frank
    Periodic event-triggered control for networked control systems based on non-monotonic Lyapunov functions2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 106, p. 35-46Article in journal (Refereed)
    Abstract [en]

    This article considers exponential stabilization of linear Networked Control Systems with periodic event-triggered control for a given network specification in terms of a maximum number of successive dropouts and a constant transmission delay. Based on stability results using non-monotonic Lyapunov functions for discontinuous dynamical systems, two sufficient results for stability of the general model of a linear event-triggered Networked Control System are derived. Those results are used to derive robust periodic event-triggered control strategies. First, a static triggering mechanism for the case without delay is derived. Afterwards, two dynamic triggering mechanisms are developed for the case without and with delay. It is shown how a degree of freedom, being contained in the dynamic triggering mechanisms, can be used to shape the resulting network traffic. The applied adaption technique is motivated by existing congestion control mechanisms in communication networks. The properties of the derived mechanisms are illustrated in a numerical example.

  • 18.
    Mitsioni, Ioanna
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Karayiannidis, Yiannis
    Division of Systems and Control, Dept. of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Stork, Johannes A.
    Center for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Data-Driven Model Predictive Control for the Contact-Rich Task of Food Cutting2019Conference paper (Refereed)
    Abstract [en]

    Modelling of contact-rich tasks is challenging and cannot be entirely solved using classical control approaches due to the difficulty of constructing an analytic description of the contact dynamics. Additionally, in a manipulation task like food-cutting, purely learning-based methods such as Reinforcement Learning, require either a vast amount of data that is expensive to collect on a real robot, or a highly realistic simulation environment, which is currently not available. This paper presents a data-driven control approach that employs a recurrent neural network to model the dynamics for a Model Predictive Controller. We build upon earlier work limited to torque-controlled robots and redefine it for velocity controlled ones. We incorporate force/torque sensor measurements, reformulate and further extend the control problem formulation. We evaluate the performance on objects used for training, as well as on unknown objects, by means of the cutting rates achieved and demonstrate that the method can efficiently treat different cases with only one dynamic model. Finally we investigate the behavior of the system during force-critical instances of cutting and illustrate its adaptive behavior in difficult cases.

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    fulltext
  • 19.
    Nikou, Alexandros
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Boskos, Dimitris
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Tumova, Jana
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    On the timed temporal logic planning of coupled multi-agent systems2018In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 97, p. 339-345Article in journal (Refereed)
    Abstract [en]

    This paper presents a fully automated procedure for controller synthesis for multi-agent systems under coupling constraints. Each agent is modeled with dynamics consisting of two terms: the first one models the coupling constraints and the other one is an additional bounded control input. We aim to design these inputs so that each agent meets an individual high-level specification given as a Metric Interval Temporal Logic (MITL). First, a decentralized abstraction that provides a space and time discretization of the multi agent system is designed. Second, by utilizing this abstraction and techniques from formal verification, we propose an algorithm that computes the individual runs which provably satisfy the high-level tasks. The overall approach is demonstrated in a simulation example conducted in MATLAB environment.

  • 20.
    Nikou, Alexandros
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Decentralized tube-based model predictive control of uncertain nonlinear multiagent systems2019In: International Journal of Robust and Nonlinear Control, ISSN 1049-8923, E-ISSN 1099-1239, Vol. 29, no 10, p. 2799-2818Article in journal (Refereed)
    Abstract [en]

    This paper addresses the problem of decentralized tube-based nonlinear model predictive control (NMPC) for a general class of uncertain nonlinear continuous-time multiagent systems with additive and bounded disturbance. In particular, the problem of robust navigation of a multiagent system to predefined states of the workspace while using only local information is addressed under certain distance and control input constraints. We propose a decentralized feedback control protocol that consists of two terms: a nominal control input, which is computed online and is the outcome of a decentralized finite horizon optimal control problem that each agent solves at every sampling time, for its nominal system dynamics; and an additive state-feedback law which is computed offline and guarantees that the real trajectories of each agent will belong to a hypertube centered along the nominal trajectory, for all times. The volume of the hypertube depends on the upper bound of the disturbances as well as the bounds of the derivatives of the dynamics. In addition, by introducing certain distance constraints, the proposed scheme guarantees that the initially connected agents remain connected for all times. Under standard assumptions that arise in nominal NMPC schemes, controllability assumptions, communication capabilities between the agents, it is guaranteed that the multiagent system is input-to-state stable with respect to the disturbances, for all initial conditions satisfying the state constraints. Simulation results verify the correctness of the proposed framework.

  • 21.
    Pinto Basto de Carvalho, Joao Frederico
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Topological Methods for Motion Prediction and Caging2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    To fulfill the requirements of automation in unstructured environmentsit will be necessary to endow robots with the ability to plan actions thatcan handle the dynamic nature of changing environments and are robust toperceptual errors. This thesis focuses on the design of algorithms to facilitatemotion planning in human environments and rigid object manipulation.Understanding human motion is a necessary first step to be able to performmotion planning in spaces that are inhabited by humans. Specifically throughlong-term prediction a robot should be able to plan collision-avoiding paths tocarry out whatever tasks are required of it. In this thesis we present a methodto classify motions by clustering paths, together with a method to translatethe resulting clusters into motion patterns that can be used to predict motion.Another challenge of robotics is the manipulation of everyday objects.Even in the realm of rigid objects, safe object-manipulation by either grippersor dexterous robotic hands requires complex physical parameter estimation.Such estimations are often error-prone and misestimations may cause completefailure to execute the desired task. Caging is presented as an alternativeapproach to classical manipulation by employing topological invariants todetermine whether an object is secured with only bounded mobility. Wepresent a method to decide whether a rigid object is in fact caged by a givengrasp or not, relying only on a rough approximation of the object and thegripper.

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  • 22.
    Pinto Basto de Carvalho, Joao Frederico
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Vejdemo-Johansson, Mikael
    CUNY College of Staten Island,Mathematics Department,New York,USA.
    Pokorny, Florian T.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Long-term Prediction of Motion Trajectories Using Path Homology Clusters2019In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2019Conference paper (Refereed)
    Abstract [en]

    In order for robots to share their workspace with people, they need to reason about human motion efficiently. In this work we leverage large datasets of paths in order to infer local models that are able to perform long-term predictions of human motion. Further, since our method is based on simple dynamics, it is conceptually simple to understand and allows one to interpret the predictions produced, as well as to extract a cost function that can be used for planning. The main difference between our method and similar systems, is that we employ a map of the space and translate the motion of groups of paths into vector fields on that map. We test our method on synthetic data and show its performance on the Edinburgh forum pedestrian long-term tracking dataset [1] where we were able to outperform a Gaussian Mixture Model tasked with extracting dynamics from the paths.

  • 23.
    Schillinger, Philipp
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. Bosch Ctr Artificial Intelligence, Renningen, Germany..
    Buerger, Mathias
    Bosch Ctr Artificial Intelligence, Renningen, Germany..
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Auctioning over Probabilistic Options for Temporal Logic-Based Multi-Robot Cooperation under Uncertainty2018In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE Computer Society, 2018, p. 7330-7337Conference paper (Refereed)
    Abstract [en]

    Coordinating a team of robots to fulfill a common task is still a demanding problem. This is even more the case when considering uncertainty in the environment, as well as temporal dependencies within the task specification. A multirobot cooperation from a single goal specification requires mechanisms for decomposing the goal as well as an efficient planning for the team. However, planning action sequences offline is insufficient in real world applications. Rather, due to uncertainties, the robots also need to closely coordinate during execution and adjust their policies when additional observations are made. The framework presented in this paper enables the robot team to cooperatively fulfill tasks given as temporal logic specifications while explicitly considering uncertainty and incorporating observations during execution. We present the effectiveness of our ROS implementation of this approach in a case study scenario.

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  • 24.
    Schlueter, Henning
    et al.
    Univ Stuttgart, Stuttgart, Germany..
    Schillinger, Philipp
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Buerger, Mathias
    Bosch Ctr Artificial Intelligence, Renningen, Germany..
    On the Design of Penalty Structures for Minimum-Violation LTL Motion Planning2018In: 2018 IEEE Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 4153-4158, article id 8619148Conference paper (Refereed)
    Abstract [en]

    This paper studies the problem of penalizing rule violation in the context of logic-based motion planning. Translating a given Linear Temporal Logic (LTL) rule into a penalty structure requires a design decision, since the discrete automata obtained from the rule do not provide a straightforward method to penalize rule violation. We propose a design method that explicitly specifies violation to allow for more flexibility in parametrization of desired behaviors and differentiation of penalty semantics. Case study results are shown in the context of an autonomous driving scenario.

  • 25.
    Selin, Magnus
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. Linkoping Univ, Dept Comp & Informat Sci, S-58183 Linkoping, Sweden.
    Tiger, Maths
    Linkoping Univ, Dept Comp & Informat Sci, S-58183 Linkoping, Sweden..
    Duberg, Daniel
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Heintz, Fredrik
    Linkoping Univ, Dept Comp & Informat Sci, S-58183 Linkoping, Sweden..
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Efficient Autonomous Exploration Planning of Large-Scale 3-D Environments2019In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 4, no 2, p. 1699-1706Article in journal (Refereed)
    Abstract [en]

    Exploration is an important aspect of robotics, whether it is for mapping, rescue missions, or path planning in an unknown environment. Frontier Exploration planning (FEP) and Receding Horizon Next-Best-View planning (RH-NBVP) are two different approaches with different strengths and weaknesses. FEP explores a large environment consisting of separate regions with ease, but is slow at reaching full exploration due to moving back and forth between regions. RH-NBVP shows great potential and efficiently explores individual regions, but has the disadvantage that it can get stuck in large environments not exploring all regions. In this letter, we present a method that combines both approaches, with FEP as a global exploration planner and RH-NBVP for local exploration. We also present techniques to estimate potential information gain faster, to cache previously estimated gains and to exploit these to efficiently estimate new queries.

  • 26. Song, Haoran
    et al.
    Haustein, Joshua Alexander
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Yuan, Weihao
    Hang, Kaiyu
    Wang, Michael Yu
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Stork, Johannes A.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Multi-Object Rearrangement with Monte Carlo Tree Search: A Case Study on Planar Nonprehensile SortingManuscript (preprint) (Other academic)
    Abstract [en]

    In this work, we address a planar non-prehensile sorting task. Here, a robot needs to push many densely packed objects belonging to different classes into a configuration where these classes are clearly separated from each other. To achieve this, we propose to employ Monte Carlo tree search equipped with a task-specific heuristic function. We evaluate the algorithm on various simulated sorting tasks and observe its effectiveness in reliably sorting up to 40 convex objects. In addition, we observe that the algorithm is capable to also sort non-convex objects, as well as convex objects in the presence of immovable obstacles.

  • 27.
    Tan, Xiao
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Berkane, Soulaimane
    Univ Quebec, Dept Informat & Ingn, Outaouais, PQ J8X 3X7, Canada..
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Constrained attitude maneuvers on SO(3): Rotation space sampling, planning and low-level control2020In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 112, article id 108659Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a novel framework that provides a systematic strategy to regulate the rigid body attitude on SO(3) within a generic constrained attitude zone. The proposed control scheme consists of three components: sampling, planning and low-level control. Specifically, an overlapping cell-like sampling for the attitude configuration space SO(3) is built and further reformulated to a graph model. Based on this abstraction, a complete graph search algorithm is utilized to generate a feasible path in the graph model. Both sufficient and necessary conditions on finding a feasible path are presented. Furthermore, to facilitate the control design, the point-to-point path is transformed into a smooth reference trajectory along the geodesics. Finally, a saturated low-level control law is formulated to robustly track the desired trajectory. Simulations demonstrate the effectiveness of the proposed control approach.

  • 28.
    Tang, Jiexiong
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Deep Learning Assisted Visual Odometry2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The capabilities to autonomously explore and interact with the environmenthas always been a greatly demanded capability for robots. Varioussensor based SLAM methods were investigated and served for this purposein the past decades. Vision intuitively provides 3D understanding of the surroundingand contains a vast amount of information that require high levelintelligence to interpret. Sensors like LIDAR, returns the range measurementdirectly. The motion estimation and scene reconstruction using camera is aharder problem. In this thesis, we are in particular interested in the trackingfrond-end of vision based SLAM, i.e. Visual Odometry (VO), with afocus on deep learning approaches. Recently, learning based methods havedominated most of the vision applications and gradually appears in our dailylife and real-world applications. Different to classical methods, deep learningbased methods can potentially tackle some of the intrinsic problems inmulti-view geometry and straightforwardly improve the performance of crucialprocedures of VO. For example, the correspondences estimation, densereconstruction and semantic representation.

    In this work, we propose novel learning schemes for assisting both directand in-direct visual odometry methods. For the direct approaches, weinvestigate mainly the monocular setup. The lack of the baseline that providesscale as in stereo has been one of the well-known intrinsic problems inthis case. We propose a coupled single view depth and normal estimationmethod to reduce the scale drift and address the issue of lacking observationsof the absolute scale. It is achieved by providing priors for the depthoptimization. Moreover, we utilize higher-order geometrical information toguide the dense reconstruction in a sparse-to-dense manner. For the in-directmethods, we propose novel feature learning based methods which noticeablyimprove the feature matching performance in comparison with common classicalfeature detectors and descriptors. Finally, we discuss potential ways tomake the training self-supervised. This is accomplished by incorporating thedifferential motion estimation into the training while performing multi-viewadaptation to maximize the repeatability and matching performance. We alsoinvestigate using a different type of supervisory signal for the training. Weadd a higher-level proxy task and show that it is possible to train a featureextraction network even without the explicit loss for it.

    In summary, this thesis presents successful examples of incorporating deeplearning techniques to assist a classical visual odometry system. The resultsare promising and have been extensively evaluated on challenging benchmarks,real robot and handheld cameras. The problem we investigate is stillin an early stage, but is attracting more and more interest from researcher inrelated fields.

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  • 29.
    Tang, Jiexiong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Ericson, Ludvig
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    GCNv2: Efficient Correspondence Prediction for Real-Time SLAM2019In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 4, no 4, p. 3505-3512Article in journal (Refereed)
    Abstract [en]

    In this letter, we present a deep learning-based network, GCNv2, for generation of keypoints and descriptors. GCNv2 is built on our previous method, GCN, a network trained for 3D projective geometry. GCNv2 is designed with a binary descriptor vector as the ORB feature so that it can easily replace ORB in systems such as ORB-SLAM2. GCNv2 significantly improves the computational efficiency over GCN that was only able to run on desktop hardware. We show how a modified version of ORBSLAM2 using GCNv2 features runs on a Jetson TX2, an embedded low-power platform. Experimental results show that GCNv2 retains comparable accuracy as GCN and that it is robust enough to use for control of a flying drone. Source code is available at: https://github.com/jiexiong2016/GCNv2_SLAM.

  • 30.
    Tang, Jiexiong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Hanme, Kim
    Vitor, Guizilini
    Sudeep, Pillai
    Rares, Ambrus.
    Neural Outlier Rejection for Self-Supervised KeypointLearning2020In: International Conference on Learning Representations(ICLR), Apr 26th through May 1st, 2020, 2020Conference paper (Refereed)
  • 31.
    Tang, Jiexiong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Rares, Ambrus
    Vitor, Guizilini
    Sudeep, Pillai
    Hanme, Kim
    Adrien, Gaidon
    Self-Supervised 3D Keypoint Learning for Ego-motionEstimation2019Report (Other academic)
  • 32.
    Theodosis, Dionysios
    et al.
    Natl Tech Univ Athens, Dept Math, Athens 15780, Greece..
    Boskos, Dimitris
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Tsinias, John
    Natl Tech Univ Athens, Dept Math, Athens 15780, Greece..
    Observer Design for Triangular Systems Under Weak Observability Assumptions2018In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 63, no 12, p. 4156-4171Article in journal (Refereed)
    Abstract [en]

    This paper presents results on the solvability of the observer design problem for general nonlinear triangular systems with inputs, under weak observability assumptions. The local state estimation is exhibited by means of a delayed time-varying Luenberger-type system. In order to achieve the global estimation, a switching sequence of observers is designed.

  • 33.
    Varava, Anastasiia
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Pinto Basto de Carvalho, Joao Frederico
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Free Space of Rigid Objects: Caging, Path Non-Existence, and Narrow Passage DetectionIn: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176Article in journal (Refereed)
    Abstract [en]

    In this work we propose algorithms to explicitly construct a conservative estimate of the configuration spaces of rigid objects in 2D and 3D. Our approach is able to detect compact path components and narrow passages in configuration space which are important for applications in robotic manipulation and path planning. Moreover, as we demonstrate, they are also applicable to identification of molecular cages in chemistry. Our algorithms are based on a decomposition of the resulting 3 and 6 dimensional configuration spaces into slices corresponding to a finite sample of fixed orientations in configuration space. We utilize dual diagrams of unions of balls and uniform grids of orientations to approximate the configuration space. We carry out experiments to evaluate the computational efficiency on a set of objects with different geometric features thus demonstrating that our approach is applicable to different object shapes. We investigate the performance of our algorithm by computing increasingly fine-grained approximations of the object's configuration space.

  • 34.
    Verginis, Christos
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Adaptive Leader-Follower Coordination of Lagrangian Multi-Agent Systems under Transient Constraints2019Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel control methodologyfor the coordination of a multi-agent system with 2nd orderuncertain Lagrangian dynamics, while guaranteeing collisionand connectivity properties in the transient state. More specifically,we consider that a leader agent aims at tracking adesired pose, while all the agents must avoid collisions witheach other. Motivated by cooperative tasks, we also considerthat a subset of the initially connected agents must remainconnected, in the sense of a connected sensing graph.We employa key property of the incidence matrix and integrate potentialfields with discontinuous adaptive control laws to compensatefor unknown dynamic parameters of the model and externaldisturbances. Simulation results in a realistic dynamics engineillustrate the theoretical findings.

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  • 35.
    Verginis, Christos
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Asymptotic Stability of Uncertain Lagrangian Systems with Prescribed Transient Response2019Conference paper (Refereed)
    Abstract [en]

    This paper considers the asymptotic trackingproblem for 2nd-order nonlinear Lagrangian systems subjectto predefined constraints for the system response, such asmaximum overshoot or minimum convergence rate. In particular,by employing discontinuous adaptive control protocolsand nonsmooth analysis, we extend previous results on funnelcontrol to guarantee at the same time asymptotic trajectorytracking from all the initial conditions that are compliant withthe given funnel. The considered system contains parametricand structural uncertainties, with no boundedness or approximation/parametric factorization assumptions. The response ofthe closed loop system is solely determined by the predefinedfunnel and is independent from the control gain selection.Finally, simulation results verify the theoretical findings.

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  • 36.
    Verginis, Christos
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Closed-Form Barrier Functions for Multi-Agent Ellipsoidal Systems With Uncertain Lagrangian Dynamics2019In: IEEE Control Systems LettersArticle in journal (Refereed)
    Abstract [en]

    Abstract—In this paper, we design a decentralized controlprotocol for the collision avoidance of a multi-agent system,which is comprised of 3D ellipsoidal agents that obey 2nd-orderuncertain Lagrangian dynamics. More specifically, we derivea novel closed-form smooth barrier function that resemblesa distance metric between 3D ellipsoids and can be used byfeedback-based control laws to guarantee inter-agent collisionavoidance. Discontinuities and adaptation laws are incorporatedin the control protocol to deal with the uncertainties of thedynamic model. The control laws are decentralized, in the sensethat each agent uses only local sensing information. Simulationresults verify the theoretical findings.

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  • 37.
    Verginis, Christos
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Mastellaro, Matteo
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Robust Cooperative Manipulation Without Force/Torque Measurements: Control Design and Experiments2019In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865Article in journal (Refereed)
    Abstract [en]

    This paper presents two novel control methodologies for the cooperative manipulation of an object by  N robotic agents. First, we design an adaptive control protocol which employs quaternion feedback for the object orientation to avoid potential representation singularities. Second, we propose a control protocol that guarantees predefined transient and steady-state performance for the object trajectory. Both methodologies are decentralized, since the agents calculate their own signals without communicating with each other, as well as robust to external disturbances and model uncertainties. Moreover, we consider that the grasping points are rigid and avoid the need for force/torque measurements. Load distribution is also included via a grasp matrix pseudoinverse to account for potential differences in the agents’ power capabilities. Finally, simulation and experimental results with two robotic arms verify the theoretical findings.

  • 38.
    Verginis, Christos
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Nikou, Alexandros
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Robust formation control in SE(3) for tree-graph structures with prescribed transient and steady state performance2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 103, p. 538-548Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel control protocol for distance and orientation formation control of rigid bodies, whose sensing graph is a static and undirected tree, in the special Euclidean group SE(3). The proposed control laws are decentralized, in the sense that each agent uses only local relative information from its neighbors to calculate its control signal, as well as robust with respect to modeling (parametric and structural) uncertainties and external disturbances. The proposed methodology guarantees the satisfaction of inter-agent distance constraints that resemble collision avoidance and connectivity maintenance properties. Moreover, certain predefined functions characterize the transient and steady state performance of the closed loop system. Finally, simulation results verify the validity and efficiency of the proposed approach.

  • 39.
    Verginis, Christos
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Vrohidis, Constantinos
    Bechlioulis, Charalampos
    Kyriakopoulos, Kostas
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Reconfigurable Motion Planning and Control in Obstacle Cluttered Environments under Timed Temporal Tasks2019In: 2019 International Conference on Robotics And Automation (ICRA): 2019 International Conference on Robotics and Automation, ICRA 2019; Palais des Congres de Montreal, Montreal; Canada; 20 May 2019 through 24 May 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 951-957, article id 8794000Conference paper (Refereed)
    Abstract [en]

    This work addresses the problem of robot navigation under timed temporal specifications in workspaces cluttered with obstacles. We propose a hybrid control strategy that guarantees the accomplishment of a high-level specification expressed as a timed temporal logic formula, while preserving safety (i.e., obstacle avoidance) of the system. In particular, we utilize a motion controller that achieves safe navigation inside the workspace in predetermined time, thus allowingus to abstract the motion of the agent as a finite timed transition system among certain regions of interest. Next, we employ standard formal verification and convex optimization techniques to derive high-level timed plans that satisfy the agent’s specifications. A simulation study illustrates and clarifies the proposed scheme.

  • 40.
    Yang, Guang-Zhong
    et al.
    Imperial Coll London, Hamlyn Ctr Robot Surg, London, England..
    Dario, Paolo
    Scuola Super Sant Anna, Biomed Robot, Pisa, Italy..
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Social robotics-Trust, learning, and social interaction2018In: Science Robotics, ISSN 2470-9476, Vol. 3, no 21, article id UNSP eaau8839Article in journal (Other academic)
  • 41.
    Yi, Xinlei
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Liu, Kun
    Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China..
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Dynamic Event-Triggered and Self-Triggered Control for Multi-agent Systems2019In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 64, no 8, p. 3300-3307Article in journal (Refereed)
    Abstract [en]

    We propose two novel dynamic event-triggered control laws to solve the average consensus problem for first-order continuous-time multiagent systems over undirected graphs. Compared with the most existing triggering laws, the proposed laws involve internal dynamic variables, which play an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno behavior. Moreover, some existing triggering laws are special cases of ours. For the proposed self-triggered algorithm, continuous agent listening is avoided as each agent predicts its next triggering time and broadcasts it to its neighbors at the current triggering time. Thus, each agent only needs to sense and broadcast at its triggering times, and to listen to and receive incoming information from its neighbors at their triggering times. It is proved that the proposed triggering laws make the state of each agent converge exponentially to the average of the agents' initial states if and only if the underlying graph is connected. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.

  • 42.
    Yuan, Weihao
    et al.
    Hong Kong Univ Sci & Technol, ECE, Robot Inst, Hong Kong, Peoples R China..
    Hang, Kaiyu
    Yale Univ, Mech Engn & Mat Sci, New Haven, CT USA..
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Wang, Michael Y.
    Hong Kong Univ Sci & Technol, ECE, Robot Inst, Hong Kong, Peoples R China..
    Stork, Johannes A.
    Orebro Univ, Ctr Appl Autonomous Sensor Syst, Orebro, Sweden..
    End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer2019In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 119, p. 119-134Article in journal (Refereed)
    Abstract [en]

    Nonprehensile rearrangement is the problem of controlling a robot to interact with objects through pushing actions in order to reconfigure the objects into a predefined goal pose. In this work, we rearrange one object at a time in an environment with obstacles using an end-to-end policy that maps raw pixels as visual input to control actions without any form of engineered feature extraction. To reduce the amount of training data that needs to be collected using a real robot, we propose a simulation-to-reality transfer approach. In the first step, we model the nonprehensile rearrangement task in simulation and use deep reinforcement learning to learn a suitable rearrangement policy, which requires in the order of hundreds of thousands of example actions for training. Thereafter, we collect a small dataset of only 70 episodes of real-world actions as supervised examples for adapting the learned rearrangement policy to real-world input data. In this process, we make use of newly proposed strategies for improving the reinforcement learning process, such as heuristic exploration and the curation of a balanced set of experiences. We evaluate our method in both simulation and real setting using a Baxter robot to show that the proposed approach can effectively improve the training process in simulation, as well as efficiently adapt the learned policy to the real world application, even when the camera pose is different from simulation. Additionally, we show that the learned system not only can provide adaptive behavior to handle unforeseen events during executions, such as distraction objects, sudden changes in positions of the objects, and obstacles, but also can deal with obstacle shapes that were not present in the training process.

  • 43.
    Yuan, Weihao
    et al.
    Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Peoples R China..
    Hang, Kaiyu
    Yale Univ, Dept Mech Engn & Mat Sci, New Haven, CT USA..
    Song, Haoran
    Hong Kong Univ Sci & Technol, Dept Mech & Aerosp Engn, Hong Kong, Peoples R China..
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Wang, Michael Y.
    Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Peoples R China.;Hong Kong Univ Sci & Technol, Dept Mech & Aerosp Engn, Hong Kong, Peoples R China..
    Stork, Johannes A.
    Örebro Univ, Ctr Appl Autonomous Sensor Syst, Örebro, Sweden.
    Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation2019In: 2019 International Conference on Robotics and Automation (ICRA) / [ed] Howard, A Althoefer, K Arai, F Arrichiello, F Caputo, B Castellanos, J Hauser, K Isler, V Kim, J Liu, H Oh, P Santos, V Scaramuzza, D Ude, A Voyles, R Yamane, K Okamura, A, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 2153-2160Conference paper (Refereed)
    Abstract [en]

    Moving a human body or a large and bulky object may require the strength of whole arm manipulation (WAM). This type of manipulation places the load on the robot's arms and relies on global properties of the interaction to succeed-rather than local contacts such as grasping or non-prehensile pushing. In this paper, we learn to generate motions that enable WAM for holding and transporting of humans in certain rescue or patient care scenarios. We model the task as a reinforcement learning problem in order to provide a robot behavior that can directly respond to external perturbation and human motion. For this, we represent global properties of the robot-human interaction with topology-based coordinates that are computed from arm and torso positions. These coordinates also allow transferring the learned policy to other body shapes and sizes. For training and evaluation, we simulate a dynamic sea rescue scenario and show in quantitative experiments that the policy can solve unseen scenarios with differently-shaped humans, floating humans, or with perception noise. Our qualitative experiments show the subsequent transporting after holding is achieved and we demonstrate that the policy can be directly transferred to a real world setting.

  • 44.
    Yuan, Weihao
    et al.
    Hong Kong Univ Sci & Technol, Hong Kong, Hong Kong, Peoples R China.;HKUST Robot Inst, Hong Kong, Hong Kong, Peoples R China.;Dept Elect & Comp Engn, Hong Kong, Hong Kong, Peoples R China..
    Stork, Johannes A.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Wang, Michael Y.
    Hong Kong Univ Sci & Technol, Hong Kong, Hong Kong, Peoples R China.;HKUST Robot Inst, Hong Kong, Hong Kong, Peoples R China.;Dept Mech & Aerosp Engn, Hong Kong, Hong Kong, Peoples R China..
    Hang, Kaiyu
    Hong Kong Univ Sci & Technol, Hong Kong, Hong Kong, Peoples R China.;HKUST Robot Inst, Hong Kong, Hong Kong, Peoples R China.;Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China.;HKUST Inst Adv Study, Hong Kong, Hong Kong, Peoples R China..
    Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning2018In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE Computer Society, 2018, p. 270-277Conference paper (Refereed)
    Abstract [en]

    Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn a nonprehensile rearrangement strategy with deep reinforcement learning based on only visual feedback. For this, we model the task with rewards and train a deep Q-network. Our potential field-based heuristic exploration strategy reduces the amount of collisions which lead to suboptimal outcomes and we actively balance the training set to avoid bias towards poor examples. Our training process leads to quicker learning and better performance on the task as compared to uniform exploration and standard experience replay. We demonstrate empirical evidence from simulation that our method leads to a success rate of 85%, show that our system can cope with sudden changes of the environment, and compare our performance with human level performance.

  • 45.
    Zhang, Heng
    et al.
    Huaihai Inst Technol, Lianyungang, Peoples R China..
    Qi, Yifei
    Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou, Zhejiang, Peoples R China..
    Wu, Junfeng
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Fu, Lingkun
    Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou, Zhejiang, Peoples R China..
    He, Lidong
    Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou, Zhejiang, Peoples R China..
    DoS Attack Energy Management Against Remote State Estimation2018In: IEEE Transactions on Big Data, ISSN 2325-5870, E-ISSN 2168-6750, Vol. 5, no 1, p. 383-394Article in journal (Refereed)
    Abstract [en]

    This paper considers a remote state estimation problem, where a sensor measures the state of a linear discrete-time process and has computational capability to implement a local Kalman filter based on its own measurements. The sensor sends its local estimates to a remote estimator over a communication channel that is exposed to a Denial-of-Service (DoS) attacker. The DoS attacker, subject to limited energy budget, intentionally jams the communication channel by emitting interference noises with the purpose of deteriorating estimation performance. In order to maximize attack effect, following the existing answer to "when to attack the communication channel", in this paper we manage to solve the problem of "how much power the attacker should use to jam the channel in each time". For the static attack energy allocation problem, when the system matrix is normal, we derive a sufficient condition for when the maximum number of jamming operations should be used. The associated jamming power is explicitly provided. For a general system case, we propose an attack power allocation algorithm and show the computational complexity of the proposed algorithm is not worse than O(T), where T is the length of the time horizon considered. When the attack can receive the real-time ACK information, we formulate a dynamic attack energy allocation problem, and transform it to a Markov Decision Process to find the optimal solution.

  • 46.
    Ótão Pereira, Pedro Miguel
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, SE-10044 Stockholm, Sweden..
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Pose stabilization of a bar tethered to two aerial vehicles2020In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 112, article id 108695Article in journal (Refereed)
    Abstract [en]

    This work focuses on the modeling, control and analysis of a bar, tethered to two unmanned aerial vehicles, which is required to stabilize around a desired pose. We derive the equations of motion of the system, we close the loop by equipping each UAV with a PID control law, and finally we linearize the closed-loop vector field around some equilibrium points of interest. When requiring the bar to stay on the horizontal plane and under no normal stress, we verify that the bar's motion is decomposable into three decoupled motions, namely a longitudinal, a lateral and a vertical: for a symmetric system, each of those motions is further decomposed into two decoupled sub-motions, one linear and one angular; for an asymmetric system, we provide relations on the UAVs' gains that compensate for the system asymmetries and which decouple the linear sub-motions from the angular sub-motions. From this analysis, we provide conditions, based on the system's physical parameters, that describe good and bad types of asymmetries. Finally, when requiring the bar to pitch or to be under normal stress, we verify that there is a coupling between the longitudinal and the vertical motions, and that a positive normal stress (tension) has a positive effect on the stability, while a negative normal stress (compression) has a negative effect on the stability.

1 - 46 of 46
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