Motion planning of skillful motions in assembly process through human demonstration

Abstract

Skillful motions in the actual assembly process are challenging for the robot to generate with conventional motion planning approaches because some states during the human assembly can be too skillful to realize automatically due to the narrow passage. To deal with this problem, this paper develops a motion planning method using the human demonstration, which can be applied to complete skillful motions in the robotic assembly process. To demonstrate conveniently without redundant third-party devices, we attach augmented reality (AR) markers to the manipulated object to track and capture its poses during the human demonstration. To overcome the problem brought by the coarse resolution of the vision system, we extract the most important key poses from the demonstration data and employ them as clues to execute motion planning to suit the target precise task. As for the selection of key poses, two policies are compared, where the first and the second derivative of the main changing parameter of every key pose serve as criteria to determine the priority of utilizing key poses. Besides, a solution to deal with colliding key poses is also proposed. The effectiveness of the presented method is verified through some simulation examples and actual robot experiments.