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  • 1.
    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, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, 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, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, 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 Maps2018Inngår i: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE Computer Society, 2018, s. 1871-1878Konferansepaper (Fagfellevurdert)
    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.

  • 2.
    Båberg, Fredrik
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Petter, Ögren
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma systen, CAS.
    Formation Obstacle Avoidance using RRT and Constraint Based Programming2017Inngår i: 2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR), IEEE conference proceedings, 2017, artikkel-id 8088131Konferansepaper (Fagfellevurdert)
    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.

  • 3.
    Carvalho, Joao Frederico
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    Vejdemo-Johansson, Mikael
    CUNY College of Staten Island, Mathematics Department, New York, USA.
    Kragic, Danica
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    Pokorny, Florian T.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    Path Clustering with Homology Area2018Inngår i: 2018 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2018, s. 7346-7353Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Path clustering has found many applications in recent years. Common approaches to this problem use aggregates of the distances between points to provide a measure of dissimilarity between paths which do not satisfy the triangle inequality. Furthermore, they do not take into account the topology of the space where the paths are embedded. To tackle this, we extend previous work in path clustering with relative homology, by employing minimum homology area as a measure of distance between homologous paths in a triangulated mesh. Further, we show that the resulting distance satisfies the triangle inequality, and how we can exploit the properties of homology to reduce the amount of pairwise distance calculations necessary to cluster a set of paths. We further compare the output of our algorithm with that of DTW on a toy dataset of paths, as well as on a dataset of real-world paths.

  • 4.
    Duberg, Daniel
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    Jensfelt, Patric
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    The Obstacle-restriction Method for Tele-operation of Unmanned Aerial Vehicles with Restricted Motion2018Inngår i: 2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV), IEEE , 2018, s. 266-273Konferansepaper (Fagfellevurdert)
    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.

  • 5. Guo, Meng
    et al.
    Bechlioulis, Charalampos P.
    Kyriakopoulos, Kostas J.
    Dimarogonas, Dimos V.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma systen, CAS.
    Hybrid Control of Multiagent Systems With Contingent Temporal Tasks and Prescribed Formation Constraints2017Inngår i: IEEE Transactions on Big Data, ISSN 2325-5870, E-ISSN 2168-6750, Vol. 4, nr 4, s. 781-792Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 6.
    Guo, Meng
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma systen, CAS.
    Boskos, Dimitris
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma systen, CAS.
    Tumova, Jana
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Dimarogonas, Dimos V.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma systen, CAS.
    Distributed hybrid control synthesis for multi-agent systems from high-level specifications2018Inngår i: Control Subject to Computational and Communication Constraints, Springer Verlag , 2018, 475, s. 241-260Kapittel i bok, del av antologi (Fagfellevurdert)
    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.

  • 7.
    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, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    Dimarogonas, Dimos V.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, 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 systems2018Inngår i: Annual Reviews in Control, ISSN 1367-5788, E-ISSN 1872-9088, Vol. 46, s. 315-325Artikkel, forskningsoversikt (Fagfellevurdert)
    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.

  • 8.
    Kokic, Mia
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Antonova, Rika
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Stork, Johannes A.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Kragic, Danica
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation2018Inngår i: Proceedings of The 2nd Conference on Robot Learning, PMLR 87, 2018, s. 641-650Konferansepaper (Fagfellevurdert)
    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.

  • 9.
    Kragic, Danica
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    From active perception to deep learning2018Inngår i: SCIENCE ROBOTICS, ISSN 2470-9476, Vol. 3, nr 23, artikkel-id eaav1778Artikkel i tidsskrift (Annet vitenskapelig)
  • 10.
    Lindemann, Lars
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik.
    Dimarogonas, Dimos V.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Feedback control strategies for multi-agent systems under a fragment at) of signal temporal logic tasks2019Inngår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 106, s. 284-293Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 11.
    Lindemann, Lars
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik.
    Dimarogonas, Dimos V.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Robust control for signal temporal logic specifications using discrete average space robustness2019Inngår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 101, s. 377-387Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 12. Linsenmayer, Steffen
    et al.
    Dimarogonas, Dimos V.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Allgoewer, Frank
    Periodic event-triggered control for networked control systems based on non-monotonic Lyapunov functions2019Inngår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 106, s. 35-46Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 13.
    Nikou, Alexandros
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma systen, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Boskos, Dimitris
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma systen, CAS.
    Tumova, Jana
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma systen, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Dimarogonas, Dimos V.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma systen, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    On the timed temporal logic planning of coupled multi-agent systems2018Inngår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 97, s. 339-345Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 14.
    Schillinger, Philipp
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre. Bosch Ctr Artificial Intelligence, Renningen, Germany..
    Buerger, Mathias
    Bosch Ctr Artificial Intelligence, Renningen, Germany..
    Dimarogonas, Dimos V.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Auctioning over Probabilistic Options for Temporal Logic-Based Multi-Robot Cooperation under Uncertainty2018Inngår i: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE Computer Society, 2018, s. 7330-7337Konferansepaper (Fagfellevurdert)
    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.

  • 15.
    Schlueter, Henning
    et al.
    Univ Stuttgart, Stuttgart, Germany..
    Schillinger, Philipp
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Buerger, Mathias
    Bosch Ctr Artificial Intelligence, Renningen, Germany..
    On the Design of Penalty Structures for Minimum-Violation LTL Motion Planning2018Inngår i: 2018 IEEE Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 4153-4158, artikkel-id 8619148Konferansepaper (Fagfellevurdert)
    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.

  • 16.
    Selin, Magnus
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, 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, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    Heintz, Fredrik
    Linkoping Univ, Dept Comp & Informat Sci, S-58183 Linkoping, Sweden..
    Jensfelt, Patric
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    Efficient Autonomous Exploration Planning of Large-Scale 3-D Environments2019Inngår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 4, nr 2, s. 1699-1706Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 17.
    Tang, Jiexiong
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    Ericson, Ludvig
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    Folkesson, John
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    Jensfelt, Patric
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    GCNv2: Efficient Correspondence Prediction for Real-Time SLAM2019Inngår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 4, nr 4, s. 3505-3512Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 18.
    Theodosis, Dionysios
    et al.
    Natl Tech Univ Athens, Dept Math, Athens 15780, Greece..
    Boskos, Dimitris
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma systen, CAS.
    Tsinias, John
    Natl Tech Univ Athens, Dept Math, Athens 15780, Greece..
    Observer Design for Triangular Systems Under Weak Observability Assumptions2018Inngår i: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 63, nr 12, s. 4156-4171Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 19.
    Verginis, Christos
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Mastellaro, Matteo
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Dimarogonas, Dimos V.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Robust Cooperative Manipulation Without Force/Torque Measurements: Control Design and Experiments2019Inngår i: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 20.
    Verginis, Christos
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Nikou, Alexandros
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Dimarogonas, Dimos V.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Robust formation control in SE(3) for tree-graph structures with prescribed transient and steady state performance2019Inngår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 103, s. 538-548Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 21.
    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, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma systen, CAS.
    Social robotics-Trust, learning, and social interaction2018Inngår i: Science Robotics, ISSN 2470-9476, Vol. 3, nr 21, artikkel-id UNSP eaau8839Artikkel i tidsskrift (Annet vitenskapelig)
  • 22.
    Yi, Xinlei
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik.
    Liu, Kun
    Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China..
    Dimarogonas, Dimos V.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Johansson, Karl H.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Dynamic Event-Triggered and Self-Triggered Control for Multi-agent Systems2019Inngår i: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 64, nr 8, s. 3300-3307Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 23.
    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, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, 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 transfer2019Inngår i: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 119, s. 119-134Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 24.
    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, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, CAS.
    Kragic, Danica
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma system, 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 Learning2018Inngår i: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE Computer Society, 2018, s. 270-277Konferansepaper (Fagfellevurdert)
    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.

  • 25.
    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, Skolan för elektroteknik och datavetenskap (EECS), Centra, Centrum för autonoma systen, CAS. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, 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 Estimation2018Inngår i: IEEE Transactions on Big Data, ISSN 2325-5870, E-ISSN 2168-6750, Vol. 5, nr 1, s. 383-394Artikkel i tidsskrift (Fagfellevurdert)
    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.

1 - 25 of 25
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