Obstacle Avoidance Planning Algorithm for Robotic Arm Motion Path Based on Fuzzy Variable Structure Compensation

The range of motion of the robotic arm can be freely adjusted, but obtaining an accurate motion law is challenging. This leads to issues such as poor planning effectiveness, long planning paths, and time-consuming planning. To address these challenges, we propose a fuzzy variable structure compensation-based algorithm for robotic arm motion path obstacle avoidance planning. Firstly, the algorithm analyzes the dynamics of the robotic arm and combines the results with fuzzy variable structure to compensate for the torque of the robotic arm. Then, it utilizes the Q-learning algorithm to calculate compensation control results and determine the best planning strategy for the path. Finally, this strategy is implemented to complete obstacle avoidance path planning for the robotic arm. Experimental results demonstrate that this algorithm provides greater accuracy, shorter planning paths, and reduced planning time.