Optimizing Robotic Arm Obstacle Avoidance via Improved Random Tree Star (RRT)* and Deep Reinforcement Learning Coordination

Driven by Industry 5.0, efficient obstacle avoidance of robotic arms in dynamic environments is a key bottleneck for human–robot collaboration in smart manufacturing. Traditional path planning methods such as Rapidly-exploring Random Tree and artificial potential field work stably in static settings but exhibit flaws including path oscillation and poor real-time performance under dynamic obstacles. Deep reinforcement learning adapts to environmental changes but is limited by low sample efficiency and high computational costs, failing industrial demands. This study proposes a collaborative framework integrating improved Rapidly-exploring Random Tree Star and Deep reinforcement learning. It uses Rapidly-exploring Random Tree Star to guide Deep reinforcement learning’s strategy exploration, reducing invalid sampling by 62%, and leverages Deep reinforcement learning’s global optimization to enhance dynamic obstacle prediction. The framework achieves a task success rate of 93.8%, surpassing traditional Rapidly-exploring Random Tree Star by 21.5%, with an average path length of 1.97 m and system energy consumption of 12.6 kWh. Experiments demonstrate superior performance in extreme dynamic scenarios, including a 94.7% success rate in multi-robot collaboration. Industrial cases confirm improvements in automobile manufacturing assembly cycle time to 8.4 s per task, yield rate to 98.7%, and reductions in energy consumption by 34% and human intervention by 85.6%, providing a reliable dynamic obstacle avoidance solution for Industry 5.0 applications.