Motion planning for vision-based stevedoring tasks on industrial robots

Abstract

This paper proposed an autonomous robot motion planner for industrial robots with a focus on vision-based stevedoring applications. The planning algorithm can be divided into two stages. The first stage generates initial geometric paths in the Cartesian space: with the 3D model of the environment and the picking and placing pose of the robot's wrist obtained by visual system, the planner finds a collision-free path using workspace cell decomposition. The second stage searches for a time-jerk optimal joints trajectories: the planner transforms the path nodes described in the Cartesian space into joints angels in the joints configuration space, then formulates and solves the optimization problem by means of cubic splines. The simulation experiments show the obvious improvement of our method with a contrast to several state-of-art algorithms in this field. And the grasping experiment verifies the practicability and effectiveness of the method on the basis of the embedded visual system and ABB120 type industrial robot.