A Novel Path Planning Method for a Hydraulic Crushing Robotic Arm Based on an Improved Informed RRT* Algorithm

Efficient and safe path planning is the core prerequisite for realizing the autonomous crushing operation of the hydraulic crushing robotic arm in the mine chute. Foundational sampling-based algorithms, specifically standard RRT* and Informed RRT*, have problems such as unattainable targets, high computational redundancy, and hydraulic commutation shock in this highly constrained context. Therefore, this paper proposes a novel path planning method based on an improved Informed RRT* algorithm. Firstly, an axis-aligned bounding box (AABB) is constructed to approximately replace the obstacles, which not only facilitates collision detection but also enables the end of the robotic arm to accurately reach the target point. Secondly, an adaptive hierarchical strategy based on inverse kinematics perception and an artificial potential field guidance mechanism are used to construct the elevated obstacle-crossing corridor, achieving dimensionality-reduced path search and reducing ineffective collision detection. Finally, cubic non-uniform B-spline and seven-segment S-shaped velocity planning are combined to complete trajectory smoothing. Simulation results show that the success rate of the proposed planning algorithm is 100%, the number of generated nodes is reduced by 90.1%, and the trajectory achieves C2 continuity, providing command-level smoothing to act as a feedforward mitigation against potential hydraulic oscillations. Path-planning experiments are carried out on the hydraulic crushing robotic arm, and the average positioning error of the end robotic arm reaching position is 30 mm, meeting the accuracy requirements and providing a reliable solution for the safe operation of heavy-duty robotic arms.