Research on Obstacle Avoidance Algorithms for Intelligent Unmanned Vehicles in Complex Environments Based on the Artificial Potential Field Method

To address the issues of intelligent unmanned vehicles (IUVs) lingering near obstacles and being prone to collisions with obstacles in complex dynamic obstacle environments when using the traditional artificial potential field method—problems caused by being too close to obstacles and excessive repulsive force—an improved artificial potential field method is proposed. A smoother repulsive force calculation formula is adopted to optimize the repulsive potential field function, resolving the issue of IUVs lingering near obstacles due to excessive repulsive force. Additionally, dynamic obstacle motion prediction, multi-directional intelligent obstacle avoidance, and a hierarchical repulsive force system are integrated to solve the problem of IUVs easily colliding with obstacles in dynamic environments. Simulation experiments were conducted to compare the proposed method with the traditional artificial potential field method and other improved algorithms. The experimental results show that the method in this study exhibits high obstacle avoidance capability in complex dynamic environments, providing an effective solution for achieving safe and efficient navigation of intelligent unmanned vehicles.