Article Details

Abstract

When applying the RRT* algorithm to UAV path planning, problems such as low path convergence efficiency, unsatisfactory path quality, and inability to avoid obstacles in a timely manner often arise. To address these issues, this paper proposes a UAV path planning method based on the integration of an improved RRT* algorithm and an improved Dynamic Window Approach (DWA). The algorithm introduces a target-attraction biased sampling strategy and incorporates directional information of the target point, guiding the expansion tree to grow more efficiently toward the goal. This significantly reduces the number of invalid sampling points. A dynamic step size method is employed to accelerate the generation of new nodes. Furthermore, the evaluation function of the DWA is improved by integrating guidance information from global path nodes into the traditional distance evaluation term, thereby enhancing trajectory prediction capability. An adaptive adjustment mechanism for evaluation weights is also incorporated to track the globally optimal path, enabling the UAV to avoid obstacles efficiently and promptly. Simulation results demonstrate that the proposed fusion planning strategy generates flight paths with shorter lengths and faster convergence, while effectively avoiding unknown obstacles.

Keywords

Unmanned Aerial Vehicle (UAV); path planning; RRT* algorithm; DWA algorithm; Integrated Algorithm

References

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About the authors

Leng Xin (1980-), female, Han ethnicity, from Mudanjiang, Heilongjiang Province, associate professor, doctoral degree, research direction: theory and methods of intelligent and optimized control, system modeling and simulation, path planning, etc; Corresponding author: Wang Yikun (1999-), male, Han nationality, from Zhoukou, Henan, master's degree, research direction: UAV path planning algorithm.