Study on Trajectory Optimization of Robot by Heuristic and Learning

A trajectory, which determines the change of the displacement and posture of a robot with time, influences dynamic torque or energy during the operation. Although various methods for optimizing the trajectories have been proposed, most of them require the exact model that is difficult to construct. Previously, the authors proposed the method for optimizing the trajectories of an industrial robot without the dynamic model by using a heuristic algorithm. The proposed method can reduce the energy or peak current value consumed in the motor. However, the proposed method cannot be available for the operation where the required path of a robot often varies, because those cases need to explore the optimal trajectory with each path, and then a quite few numbers of driving the actual robot for exploring will be required. Therefore, this paper proposed a method for instantaneously optimizing the trajectories by using a neural network (NN) that is constructed by using the optimal trajectories obtained with the heuristic algorithm. In this paper, first, the optimal start position and the optimal operation times of an industrial robot is respectively explored by the heuristic algorithm using the average power consumed in the motor as the evaluation value. Next, a NN learned the parameters of the optimal trajectories explored by the heuristic algorithm for various paths. The constructs NN will generate optimal trajectories that reduce the evaluation values to the same extent as the trajectories explored by the heuristic algorithm. Namely, the proposed method using the NN can estimate the optimal trajectories in a shorter time than the method using the heuristic algorithm.