Global Time-energy Optimal Planning of Industrial Robot Trajectories

Abstract

A technique for optimal trajectory planning of robot manipulators is presented. It consists of linking two points in the operational space while minimizing a cost function, taking into account dynamic equations of motion as well as bounds on joint velocities, accelerations, jerks and force/torques. The cost function is used as a weighted balance of traveling time and mechanical energy of the actuators. Also, a novel ranking technique for the penalty function is designed to deal with the constraints. Furthermore, the environment-gene evolutionary immune clonal algorithm (EGICA) is proposed to solve the optimization problem. The use of the above-mentioned strategy makes the algorithm have a certain learning ability and enhances its global searching ability, thus improving solution quality and algorithm efficiency. The algorithm is tested with Stanford robot in simulation, and the result shows that the presented method has higher search efficiency and can obtain better solution in comparison with the existing methods.