Motion Planning through Demonstration to Deal with Complex Motions in Assembly Process

Abstract

Complex and skillful motions in actual assembly process are challenging for the robot to generate with existing motion planning approaches, because some key poses during the human assembly can be too skillful for the robot to realize automatically. In order to deal with this problem, this paper develops a motion planning method using skillful motions from demonstration, which can be applied to complete robotic assembly process including complex and skillful motions. In order to demonstrate conveniently without redundant thirdparty devices, we attach augmented reality (AR) markers to the manipulated object to track and capture poses of the object during the human assembly process, which are employed as key poses to execute motion planning by the planner. Derivative of every key pose serves as criterion to determine the priority of use of key poses in order to accelerate the motion planning. The effectiveness of the presented method is verified through some numerical examples and actual robot experiments.

Path Planning for Manipulation using Experience-driven Random Trees

“…Additionally, we discuss how to select the best candidate for our planner given a library of path experiences. Such experience selection strategy enables the use of our planners in frameworks that incrementally build libraries of experiences by adding newly computed motion plans (e.g., [11], [14]), as well as in systems that gather experiences from human demonstrations (e.g., [15]- [17]).…”