A Novel Algorithm of Human-Like Motion Planning for Robotic Arms

Abstract

As robots get closer to humans, higher requests to robots are put forward. Human-like motion is one of those important issues, especially for humanoid service robots, advanced industrial robots and assistive robots. In this paper, a motion-decision algorithm is proposed and applied to human-like motion planning of robotic arms. The algorithm consists of two parts: intelligent decision and calculation of the joint trajectory. The former includes two parts: the hierarchical planning strategy (HPS) and the Bayesian decision. The HPS reflects the general rules of human arm movements and the robotic arms using the HPS can simulate the movements of human arms accurately. The Bayesian decision is used to make robotic arms choose an appropriate mode of motion. The calculation of the joint trajectory builds a motion framework of robotic arms to generate human-like movements. The human performance measures (HPMs) in different planning hierarchies are proposed. Finally, the validity of the proposed algorithm is verified by experiments.