Bidirectional rapidly exploring random tree path planning algorithm based on adaptive strategies and artificial potential fields

Path planning is central to the operation of intelligent systems such as robots, drones, and autonomous vehicles, where path performance and time efficiency directly impact overall system performance. Although sampling- based path planning methods have achieved significant success in this field, their performance remains limited in crowded environments. This paper combines and improves the bidirectional exploration method of BIRRT* (Bidirectional Rapidly-exploring Random Tree Star) and the expansion guidance of APF-RRT* (Artificial Potential Field Rapidly-exploring Random Tree Star), proposing a bidirectional rapidly exploring random tree algorithm based on adaptive mechanisms and artificial potential fields (AB-APF-RRT*). This method improves both the sampling and expansion methods of RRT*(Rapidly-exploring Random Tree Star) . In terms of sampling, the probabilities indifferent regions are modified using the line connecting the start and goal points, and dynamic goal bias and opposing bias strategies are introduced to guide the trees towards the target and each other. In terms of expansion, based on the bidirectional exploration of the two trees, optimized artificial potential fields and ray-casting navigation strategies are applied to guide the trees towards the goal while avoiding obstacles and dynamically adjusting the step size. To enhance the smoothness of the path, a cubic spline interpolation method is further applied. Ultimately, a comparison with several popular sampling-based path planning algorithms demonstrates that this method excels in both performance and time efficiency.