Optimization Algorithm for 3D Smooth Path of Robotic Arm in Dynamic Obstacle Environments

With the rapid development of industrial automation, robotic arms are increasingly required to operate in dynamic and unstructured environments. Path planning for such systems has become a critical challenge, particularly in scenarios involving dynamic obstacles and complex environments. This paper addresses the challenge of obstacle avoidance path planning for robotic arms in dynamic and complex environments. It introduces a hybrid trajectory planning method that combines the strengths of the rapidly exploring random tree (RRT) and artificial potential field (APF) algorithms. The method divides the process into two phases: initial static path planning and dynamic path replanning. An adaptive angle guidance strategy is proposed to enhance the efficiency of RRT in static environments by improving target directionality and reducing redundant nodes, followed by Catmull–Rom spline interpolation for path smoothing. For dynamic environments, the algorithm dynamically adjusts the weight factors of RRT and APF, enabling rapid local path replanning to avoid both static and dynamic obstacles. Experimental results demonstrate that the proposed method achieves significant improvements in trajectory smoothness, path length, and computational efficiency compared to traditional approaches. This work offers a robust solution for real-time motion planning, contributing to the advancement of robotic arm control in industrial and unstructured settings.