Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm

Abstract

Nowadays, most robotic systems perform their tasks in an environment that is generally known. Thus, robot’s trajectory can be planned in advance depending on a given task. However, as a part of modern manufacturing systems which are faced with the requirements to produce high product variety, mobile robots should be flexible to adapt to changing and diverse environments and needs. In such scenarios, a modification of the task or a change in the environment, forces the operator to modify robot’s trajectory. Such modification is usually expensive and time-consuming, as experienced engineers must be involved to program robot’s movements. The current paper presents a solution to this problem by simplifying the process of teaching the robot a new trajectory. The proposed method generates a trajectory based on an initial raw demonstration of its shape. The new trajectory is generated in such a way that the errors between the actual and target end positions and orientations of the robot are minimized. To minimize those errors, the grey wolf optimization (GWO) algorithm is applied. The proposed approach is demonstrated for a two-wheeled mobile robot. Simulation and experimental results confirm high accuracy of generated trajectories.