Evolutionary optimization of cubic polynomial joint trajectories for industrial robots

The conventional approach to find the constrained minimum-time path for a robot manipulator employs the trial-and-error procedure, namely the flexible polyhedron search method. In this paper we introduce an alternative approach by applying the genetic search algorithms to schedule the time intervals between each pair of adjacent knots such that the total traveling time is minimized subjected to the physical constraints on joint velocities, accelerations, and jerks. Modified heuristic crossover and a scaled and normed performance measure are applied to the genetic algorithmic searching procedures. Experiments with different combinations of crossover rates and mutation rates are carried out and the corresponding results outweigh the constrained minimum-time obtained from the trial-and-error polyhedron search method.