Fitness biasing to produce adaptive gaits for hexapod robots

Anytime learning with fitness biasing was shown in an earlier work to be an effective tool for learning leg cycles for a hexapod robot. This learning system was capable of adapting to changes in the environment. Although the leg cycles were appropriate for rougher terrain, the gaits produced with th...

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Bibliographic Details
Published in2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) Vol. 3; pp. 2843 - 2848 vol.3
Main Author Parker, G.B.
Format Conference Proceeding
LanguageEnglish
Published Piscataway NJ IEEE 2004
Subjects
Applied sciences
Computer science
Computer science; control theory; systems
Control theory. Systems
Exact sciences and technology
Genetic algorithms
Learning systems
Leg
Legged locomotion
Neural networks
Oscillators
Robot kinematics
Robot sensing systems
Robotics
Testing
Gait
Parallel mechanism
Bearing(mechanics)
Experimental study
Adaptive method
Robotics
Leg
Posture
Genetic algorithm
Linkage mechanism
Hexapod
Artificial intelligence
Robot
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Summary:Anytime learning with fitness biasing was shown in an earlier work to be an effective tool for learning leg cycles for a hexapod robot. This learning system was capable of adapting to changes in the environment. Although the leg cycles were appropriate for rougher terrain, the gaits produced with them by a standard genetic algorithm were not capable of bearing the robot's load. In this paper, we present the use of anytime learning with fitness biasing to improve the gaits produced by allowing the learning system to adapt to unforeseen changes in the environment and the robot's capabilities. Training and tests were done in simulation, with the resultant gaits tested on the actual robot.
ISBN:9780780384637
0780384636
DOI:10.1109/IROS.2004.1389840