A Sampling-Based Motion Planning Strategy for Robotic Manipulators in Highly Dynamic Workspaces

Abstract

With the increasing deployment of collaborative robots, motion planning in shared human-robot workspaces has gained growing theoretical and practical significance. This paper focuses on the development of a trajectory planning algorithm for robotic arms under environmental constraints. Aiming at the problems caused by traditional trajectory planning algorithms, such as slow convergence speed and the lack of the consideration of obstacles along the path of the robotic arm, a dynamic obstacle avoidance trajectory planning algorithm based on the configuration and kinematic model of the robotic arm is proposed, which enables the robotic arm to dynamically plan a smooth trajectory from the start node to the goal node while avoiding encountering obstacles in complex constrained environments. Based on the results of the Informed RRT* algorithm, the algorithm integrates single-step path optimization, path refinement and quintic polynomial trajectory planning process to realize the shortest path planning and smooth trajectory generation while avoiding encountering obstacles. Simulation results on a 6-degree-of-freedom robotic arm validate the effectiveness of the proposed algorithm in obstacle avoidance and trajectory planning within constrained environments in three-dimensional space. Compared to traditional algorithms, the proposed algorithm demonstrates faster convergence speed and more comprehensive obstacle avoidance capabilities for the whole robotic arm.