TY - GEN
T1 - A motion planning scheme for cooperative loading using heterogeneous robotic agents
AU - Logothetis, Michalis
AU - Vlantis, Panagiotis
AU - Vrohidis, Constantinos
AU - Karras, George C.
AU - Kyriakopoulos, Kostas J.
N1 - Funding Information:
This work was supported by funding from the EU H2020 Research and Innovation Programme under GA No. 731869 (Co4Robots). The presentation of this paper was partially made possible through a travel grant by the ”C. Mavroidis Award of Excellence in Robotics and Automation” at the NTUA.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In this work, we present a decentralized motion planning and control architecture for the cooperative loading task using heterogeneous robotic agents operating in a cluttered workspace with static obstacles. Initially, we tackle the problem of calculating a set of feasible loading configurations via a Probabilistic Road Maps technique. Next, an optimal loading configuration is selected considering the connectivity of the space and the Euclidean distance between the robotic agents. A motion control scheme for each agent is designed and implemented in order to autonomously guide each robot to the desired loading configuration with guaranteed obstacle avoidance and convergence properties. The performance and the applicability of the proposed strategy is experimentally verified in a variety of loading scenarios using a redundant static manipulator and a mobile platform.
AB - In this work, we present a decentralized motion planning and control architecture for the cooperative loading task using heterogeneous robotic agents operating in a cluttered workspace with static obstacles. Initially, we tackle the problem of calculating a set of feasible loading configurations via a Probabilistic Road Maps technique. Next, an optimal loading configuration is selected considering the connectivity of the space and the Euclidean distance between the robotic agents. A motion control scheme for each agent is designed and implemented in order to autonomously guide each robot to the desired loading configuration with guaranteed obstacle avoidance and convergence properties. The performance and the applicability of the proposed strategy is experimentally verified in a variety of loading scenarios using a redundant static manipulator and a mobile platform.
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U2 - 10.1109/ICRA.2019.8794323
DO - 10.1109/ICRA.2019.8794323
M3 - Conference contribution
AN - SCOPUS:85071425254
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 9660
EP - 9666
BT - 2019 International Conference on Robotics and Automation, ICRA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Robotics and Automation, ICRA 2019
Y2 - 20 May 2019 through 24 May 2019
ER -