Adaptive Real-Time Collision-Free Trajectory Planning for Multi Serial Robots with Dynamic Obstacles in Collaborative Workspace

Abstract

This study proposes a novel real-time trajectory planning framework designed to address the complex navigation challenges of collaborative robots operating in shared workspaces. The developed framework effectively integrates Model Predictive Path Integral (MPPI) control with Artificial Potential Field (APF) mechanisms to ensure robust and adaptive motion generation. To comprehensively evaluate its performance, the approach was validated in diverse environments, including no obstacle, high multi obstacle, and wall configurations, using complex setups of two, three, and four UR5 manipulators. Throughout these extensive tests, the proposed algorithm consistently achieved a 100% success rate in real-time collision avoidance while strictly adhering to the inherent kinematic limits of the robots. Comparative analyses further highlighted the superior efficiency of this method. In the most challenging environment, the proposed framework outperformed a baseline jerk-and-acceleration controller by completing operations 21.3% faster with a 3.8% lower jerk, and significantly surpassed a pure acceleration controller with a 41% faster operation time alongside a 16.7% reduction in jerk.