Path Planning of Manipulators Based on the Norm Adaptive Step-size RRT Algorithm

A norm adaptive step size rapidly-exploring random tree （RRT） algorithm suitable for manipulators was proposed to address the issues of fixed step size debugging time, poor collision detection performance, and low search efficiency of the traditional RRT algorithm in multidimensional environments. Firstly, a kinematic model with a 6-degree-of-freedom UR5 manipulator was established, and the forward kinematic analysis was performed. Secondly, by combining the norm inequality and the Jacobian matrix, a step mapping relation between the workspace of the manipulator and the joint space was constructed, dynamically changing the search step size of the joint space while ensuring the effectiveness of collision detection. Finally, the simulation analysis results show that the norm adaptive step size RRT algorithm has higher search efficiency than the traditional fixed step size RRT algorithm and does not require the manual step size adjustment, and the path search time has been reduced by 29.32%, thus improving the efficiency of manipulator path planning.