Simulation of obstacle avoidance planning based on improved RRTstar robotic arm

Abstract An obstacle avoidance pathfinding strategy with an augmented RRT* algorithm was presented for the robotic arm’s operation. A simulation model of the robotic arm was created. The Monte Carlo method was employed to solve its workspace. To mitigate the weaknesses of the RRT* algorithm, such as slow convergence and poor orientation, the algorithm was improved by combining gravitational and repulsive force fields from the artificial potential field method with a dynamic step size strategy. Simulation experiments were carried out using MATLAB . The results showed that the revised algorithm lessened the path search time by 52.1% and the path search length by 9.1% as opposed to the original RRT* algorithm. The experiments were verified in the ROS backdrop. The proposed algorithm was found to be more advantageous for robotic arm obstacle avoidance.