Obstacle Avoidance Algorithm Program for Different Shape Obstacles Based on Improved Artificial Potential Field Method

This article presents an obstacle avoidance algorithm program based on an improved artificial potential field method designed to handle obstacles of various geometric shapes. The core concept involves constructing a potential field containing both repulsive forces from obstacles and attractive forces toward the target point. Through mathematical modeling of these forces, the algorithm enables autonomous robot navigation by guiding the robot through the potential field to reach the target while avoiding obstacles. In our research, we have enhanced the traditional artificial potential field method with several key improvements: implementing shape-aware repulsive field calculations that can handle polygonal, circular, and irregular obstacles; adding dynamic parameter adjustment mechanisms to prevent local minima; and incorporating velocity-based force modulation for smoother trajectories. The algorithm implementation includes critical functions such as distance-to-obstacle computation, force vector summation, and path optimization routines. We have conducted comprehensive testing and validation of our algorithm, comparing its performance with other obstacle avoidance methods. The results demonstrate superior performance across various scenarios, particularly in environments with complex obstacle configurations. The program features modular code structure with separate modules for obstacle representation, potential field generation, and motion planning, making it easily adaptable for different robotic platforms. This article provides detailed insights into the algorithm's theoretical foundation, implementation specifics, and practical applications, offering valuable knowledge for researchers and developers in autonomous navigation systems.