Path Planning for Collaborative Robot under Complex Biomedical Lab Environment

Abstract

Collaborative robots are widely utilized in biomedical labs to assist or even replace humans to conduct operations to promote efficiency and accuracy. Robot assisted operations also provide improved safety when dealing with biochemical or biomedical research. To achieve advanced robot assisted collaborative operations, a fast, efficient, and intelligent path planning algorithm is necessary. Current methods lack specific considerations for the complex biomedical lab environment, resulting in relatively slow, long, and potentially dangerous paths. To address these issues, this paper proposes a new path planing algorithm based on classical rapidly exploring random tree method. Adaptive searching step size, target orientated searching direction, and further path optimization to avoid obstacle outline following are added to obtain faster and safer robot motion paths in biomedical labs. Finally, Matlab and ROS/Gazebo simulations are conducted to demonstrate the efficacy and efficiency of the proposed method.