MMD-RRT: a path planning strategy for robotic arm with improved RRT algorithm in unstructured environments

Abstract Robotic arm path planning in complex, unstructured environments often suffers from challenges such as excessive sampling randomness, low search efficiency, and redundant path nodes. To address these shortcomings, this paper introduces multi-mode dynamic sampling rapidly-exploring random tree (MMD-RRT), an enhanced path planning method based on the RRT algorithm. MMD-RRT dynamically adjusts its sampling strategy and step size in response to obstacles. Key improvements include: (1) a novel multi-mode dynamic sampling strategy that effectively reduces the randomness of sample points; (2) a dynamic window with variable step sizes designed to adapt to diverse environments and improve search efficiency; and (3) path optimization using a greedy strategy to eliminate redundant nodes. Experimental results show that, compared to traditional algorithms in unstructured environments, MMD-RRT reduces path length by an average of 24.89% and search time by an average of 75.90%. Validation using an XArm6 robotic arm confirms the algorithm’s ability to generate effective obstacle avoidance paths, ensuring the robot completes tasks safely and efficiently.