Planning multiple paths with evolutionary speciation

Abstract

This paper demonstrates a new approach to multidimensional path planning that is based on multiresolution path representation, where explicit configuration space computation is not required, and incorporates an evolutionary algorithm for solving the multimodal optimization problem, generating multiple alternative paths simultaneously. The multiresolution path representation reduces the expected search length for the path-planning problem and accordingly reduces the overall computational complexity. Resolution-independent constraints due to obstacle proximity and path length are introduced into the evaluation function. The resulting path-planning system has been evaluated on problems of two, three, four, and six degrees of freedom. The resulting paths are practical, consistent, and have acceptable execution times. The system can be applied for planning paths for mobile robots, assembly, and articulated manipulators. Generation of multiple alternative paths is an example of multimodal search and, in our pr evious work, a new approach to multimodal function optimization has been developed using a genetic algorithm (GA) with minimal representation size cluster analysis. This multiple-population GA identifies different species and is used as the basis for an evolutionary multipath-planning algorithm which generates multiple alternative paths simultaneously. The multipath algorithm is demonstrated on a number of two-dimensional path-planning problems.