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为降低无人机在拒止条件下的规划路径长度，并保证无人机的安全飞行，提出了一种基于改进A-star的路径规划算法。该算法在进行轨迹规划时不再完全依赖于A-star算法进行搜索，而是在充分利用最小化Snap算法规划速度较快的基础上，通过引入B样条曲线增加轨迹的控制点，最终通过改进A-star算法将控制点推离障碍物得到最终轨迹。不考虑障碍物的最小化Snap算法减少了路径搜索的时间，而通过将控制点推离障碍物的改进A-star算法得到了一条平滑的轨迹，克服了拒止条件下无人机路径规划时间慢和传统A-star算法包含许多尖锐拐点的缺点。仿真结果表明，在简单障碍物环境中，该算法在生成路径的平滑度和路径长度都优于A-star算法；在复杂障碍物环境中，该算法在收敛速度和生成路径的时间和路径长度方面都优于Fast-planner算法。在安全性方面，所提出的算法在自主导航中的成功率明显高于传统A-star算法的成功率。
Abstract：To reduce the path length of drones under deny conditions and ensure their safe flight, a path planning algorithm based on an improved A-star method is proposed. In this algorithm, trajectory planning does not rely solely on the A-star algorithm for search. Instead, it utilizes the minimum Snap algorithm for faster speed planning and introduces B-spline curves to increase the number of control points in the trajectory.Ultimately, the improved A-star algorithm is used to adjust these control points away from obstacles to obtain the final trajectory. The minimum Snap algorithm, which does not consider obstacles, reduces the time required for path search. By using the improved A-star algorithm to push control points away from obstacles, a smooth trajectory is achieved. This approach addresses the drawbacks of slow path planning time under deny conditions and the sharp corners typically found in traditional A-star algorithm paths. Simulation results show that, in simple obstacle environments, this algorithm outperforms the A-star algorithm in both path smoothness and length. In complex obstacle environments, it surpasses the Fast-planner algorithm in convergence speed,path generation time, and path length. In terms of safety, the proposed algorithm significantly improves the success rate in autonomous navigation compared to the traditional A-star algorithm.
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基本信息:
DOI：10.16338/j.issn.2097-0714.20240078
中图分类号:V279;V249
引用信息:
[1]王建园,符洋,陈金宝,等.基于改进A-star的无人机路径规划算法[J].空天技术,2025,No.464(02):78-87.DOI:10.16338/j.issn.2097-0714.20240078.
基金信息:
国家自然科学基金企业创新发展联合基金集成项目（U21B6002）; 上海航天科技创新基金（SAST2023-006）