Global time-energy optimal planning of 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. We choose the cost function 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 clonal environmental mutation operator performed in EGICA can produce the improvement of the immune efficiency and a self-studying ability of the algorithm. With the schemes used above, a global optimum can be achieved. The algorithm is tested in simulation yielding good results.