Co-evolution of active sensing and locomotion gaits of simulated snake-like robot
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Genetic programming based automatic gait generation for quadruped robots
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Symbiotic robot organisms: REPLICATOR and SYMBRION projects
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
A review of gait optimization based on evolutionary computation
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
Online Optimization of Swimming and Crawling in an Amphibious Snake Robot
IEEE Transactions on Robotics
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Manual design of motion patterns for legged robots is difficult task often with suboptimal results. To automate this process variety of approaches have been tried including various evolutionary algorithms. In this work we present an algorithm capable of generating viable motion patterns for multi-legged robots. This algorithm consists of two evolutionary algorithms working in co-evolution. The GP is evolving motion of a single leg while the GA deploys the motion to all legs of the robot. Proof-of-concept experiments show that the co-evolutionary approach delivers significantly better results than those evolved for the same robot with simple genetic programming algorithm alone.