TY - GEN
T1 - Evolutionary synthesis of dynamic motion and reconfiguration process for a modular robot M-TRAN
AU - Yoshida, Eiichi
AU - Murata, Satoshi
AU - Kamimura, Akiya
AU - Tomita, Kohji
AU - Kurokawa, Haruhisa
AU - Kokaji, Shigeru
N1 - Publisher Copyright:
© 2003 IEEE.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2003
Y1 - 2003
N2 - In this paper we present a couple of evolutionary motion generation methods using genetic algorithms (GA) for self-reconfigurable modular robot M-TRAN and demonstrate their effectiveness through hardware experiments. Using these methods, feasible solutions with sufficient performance can be derived for a motion generation problem with high complexity coming from huge configuration and motion possibilities of the robot. The first method called ERSS (Evolutionary Reconfiguration Sequence Synthesis) applies GA (Genetic Algorithm) to evolution of motion sequence including configuration changes though natural genetic representation. The effectiveness of the generated full-body dynamic motions are verified through hardware experiments. The second method called ALPG (Automatic Locomotion Pattern Generation) Method seeks locomotion pattern using a neural oscillator as a CPG (Central Pattem Generator) model and GA to optimize the parameters for locomotion. A number of efficient locomotion patterns has been derived, which are also experimentally verified.
AB - In this paper we present a couple of evolutionary motion generation methods using genetic algorithms (GA) for self-reconfigurable modular robot M-TRAN and demonstrate their effectiveness through hardware experiments. Using these methods, feasible solutions with sufficient performance can be derived for a motion generation problem with high complexity coming from huge configuration and motion possibilities of the robot. The first method called ERSS (Evolutionary Reconfiguration Sequence Synthesis) applies GA (Genetic Algorithm) to evolution of motion sequence including configuration changes though natural genetic representation. The effectiveness of the generated full-body dynamic motions are verified through hardware experiments. The second method called ALPG (Automatic Locomotion Pattern Generation) Method seeks locomotion pattern using a neural oscillator as a CPG (Central Pattem Generator) model and GA to optimize the parameters for locomotion. A number of efficient locomotion patterns has been derived, which are also experimentally verified.
KW - CPG
KW - Evolutionary computation
KW - Genetic algorithms
KW - Modular robotics
KW - Motion generation
KW - Self-reconfiguration
UR - http://www.scopus.com/inward/record.url?scp=34648864005&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34648864005&partnerID=8YFLogxK
U2 - 10.1109/CIRA.2003.1222317
DO - 10.1109/CIRA.2003.1222317
M3 - Conference contribution
AN - SCOPUS:34648864005
T3 - Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
SP - 1004
EP - 1010
BT - Proceedings - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2003
Y2 - 16 July 2003 through 20 July 2003
ER -