Research on High-Precision Motion Planning of Large Multi-Arm Rock Drilling Robot Based on Multi-Strategy Sampling Rapidly Exploring Random Tree*

In addressing the optimal motion planning issue for multi-arm rock drilling robots, this paper introduces a high-precision motion planning method based on Multi-Strategy Sampling RRT* (MSS-RRT*). A dual Jacobi iterative inverse solution method, coupled with a forward kinematics error compensation model, is introduced to dynamically correct target positions, improving end-effector positioning accuracy. A multi-strategy sampling mechanism is constructed by integrating DRL position sphere sampling, spatial random sampling, and goal-oriented sampling. This mechanism flexibly applies three sampling methods at different stages of path planning, significantly improving the adaptability and search efficiency of the RRT* algorithm. In particular, DRL position sphere sampling is prioritized during the initial phase, effectively reducing the number of invalid sampling points. For training a three-arm DRL model with the twin delayed deep deterministic policy gradient algorithm (TD3), the Hindsight Experience Replay-Obstacle Arm Transfer (HER-OAT) method is used for data replay. The cylindrical bounding box method effectively prevents collisions between arms. The experimental results show that the proposed method improves motion planning accuracy by 94.15% compared to a single Jacobi iteration. MSS-RRT* can plan a superior path in a shorter duration, with the planning time under optimal path conditions being only 20.71% of that required by Informed-RRT*, and with the path length reduced by 21.58% compared to Quick-RRT* under the same time constraints.