TY - CHAP
T1 - Distributed Adaptive Locomotion Learning in ModRED Modular Self-reconfigurable Robot
AU - Dutta, Ayan
AU - Dasgupta, Prithviraj
AU - Nelson, Carl
N1 - Publisher Copyright:
© 2018, Springer International Publishing AG.
PY - 2018
Y1 - 2018
N2 - We study the problem of adaptive locomotion learning for modular self-reconfigurable robots (MSRs). MSRs are mostly used in unknown and difficult-to-navigate environments where they can take a completely new shape to accomplish the current task at hand. Therefore it is almost impossible to develop the control sequences for all possible configurations with varying shape and size. The modules have to learn and adapt their locomotion in dynamic time to be more robust in nature. In this paper, we propose a Q-learning based locomotion adaptation strategy which balances exploration versus exploitation in a more sophisticated fashion. We have applied our proposed strategy mainly on the ModRED modular robot within the Webots simulator environment. To show the generalizability of our approach, we have also applied it on a Yamor modular robot. Experimental results show that our proposed technique outperforms a random locomotion strategy and it is able to adapt to module failures.
AB - We study the problem of adaptive locomotion learning for modular self-reconfigurable robots (MSRs). MSRs are mostly used in unknown and difficult-to-navigate environments where they can take a completely new shape to accomplish the current task at hand. Therefore it is almost impossible to develop the control sequences for all possible configurations with varying shape and size. The modules have to learn and adapt their locomotion in dynamic time to be more robust in nature. In this paper, we propose a Q-learning based locomotion adaptation strategy which balances exploration versus exploitation in a more sophisticated fashion. We have applied our proposed strategy mainly on the ModRED modular robot within the Webots simulator environment. To show the generalizability of our approach, we have also applied it on a Yamor modular robot. Experimental results show that our proposed technique outperforms a random locomotion strategy and it is able to adapt to module failures.
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U2 - 10.1007/978-3-319-73008-0_24
DO - 10.1007/978-3-319-73008-0_24
M3 - Chapter
AN - SCOPUS:85063305213
T3 - Springer Proceedings in Advanced Robotics
SP - 345
EP - 357
BT - Springer Proceedings in Advanced Robotics
PB - Springer Science and Business Media B.V.
ER -