An evolutionary algorithm with non-random initial population for path planning of manipulators

Abstract

In this paper, a hierarchical evolutionary algorithm is proposed for the path planning of manipulators. The proposed algorithm consists of a global path planner (GPP) and a local motion planner (LMP). The global planner, a MAKLINK based approach, plans a trajectory for a robot end-effector from a starting free-space to goal free-space. An evolutionary algorithm with a non-random initial population is adopted to plan the manipulator configurations along a path given by the former stage. Once the optimal configuration is obtained by the evolutionary algorithm, the optimal chromosomes will be reserved as the initial population. Since the initial population is non-random, the evolution is more efficient and the planned path is smoother than traditional GA. Simulation results show that the proposed algorithm works well, specifically in terms of collision avoidance and computation efficiency. © 2009 Springer Berlin Heidelberg.