Authors:
Jan Černý
and
Jiří Kubalík
Affiliation:
Czech Technical University, Czech Republic
Keyword(s):
Evolutionary Algorithms, Genetic Programming, Legged Robots, Robotic Gait, Co-evolution, Motion Patterns, Evolutionary Robotics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Co-Evolution and Collective Behavior
;
Computational Intelligence
;
Evolutionary Computing
;
Evolutionary Robotics and Intelligent Agents
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
Recently, a new co-evolutionary approach for generating motion patterns for multi-legged robots which exhibit
symmetry and module repetition was proposed. The algorithm consists of two evolutionary algorithms
working in co-evolution. The first one, a genetic programming module, evolves a motion of a single leg. The
second one, a genetic algorithm module, seeks for the optimal deployment of the single-leg motion pattern
to all legs of the robot. Thus, the whole task is decomposed into two subtasks that are to be solved simultaneously.
First proof-of-concept experiments proved such a decomposition helps to produce better solutions
than a simple GP-based approach that tries to evolve individual motion patterns for all legs of the robot. This
paper further analyzes the co-evolutionary algorithm focusing on two things – the way it handles the problem
decomposition and the type of functions it uses to control joints of the robot. The experiments carried out in
this work indicate that both
design choices positively contribute to its performance.
(More)