在无人艇路径规划算法中，针对传统蚁群算法收敛速度慢、路径拐点多以及传统动态窗口算法不能适应复杂避碰环境等问题，本文提出一种融合改进蚁群和动态窗口法的无人艇避障路径规划算法。首先，通过优化启发函数并改进初始信息素浓度来提高算法的收敛速度；其次，将转向角参数θ引入状态转移概率公式、改进状态转移规则并对路径进行优化处理，提高了路径的平滑性且减少了冗余点；进一步引入模糊控制的方法来改进动态窗口法的轨迹评价函数；从而实现无人艇全局路径优化并有效进行局部动态避碰。最后通过Matlab进行仿真实验。仿真结果表明，算法迭代次数缩小了38.1%、拐点数量减少了72.7%、路程缩短1%左右，未知及动态障碍物的避碰效果有明显提升。
In the path planning algorithm for unmanned surface vessels (USVs), aiming at the problems of slow convergence speed, excessive path turning points in the traditional ant colony algorithm, and the inability of the traditional dynamic window algorithm to adapt to complex collision avoidance environments, this paper proposes a USV obstacle avoidance path planning algorithm that integrates an improved ant colony algorithm and dynamic window method. Firstly, the convergence speed of the algorithm is enhanced by optimizing the heuristic function and improving the initial pheromone concentration. Secondly, the turning angle parameter θ is introduced into the state transition probability formula, the state transition rule is improved, and the path is optimized to enhance the smoothness of the path and reduce redundant points. Further, the fuzzy control method is introduced to improve the trajectory evaluation function of the dynamic window method. Thus, the global path of the USV is optimized and local dynamic collision avoidance is effectively carried out. Finally, simulation experiments are conducted using Matlab. The simulation results show that the number of algorithm iterations is reduced by 38.1%, the number of turning points is reduced by 72.7%, the distance is shortened by about 1%, and the collision avoidance effect for unknown and dynamic obstacles is significantly improved.
2025,47(22): 102-110 收稿日期：2025-2-26
DOI：10.3404/j.issn.1672-7649.2025.22.015
分类号：U692.3+1
基金项目：国家自然科学基金面上项目（52178067）
作者简介：段修洋（2001 – ），男，硕士研究生，研究方向为船舶避碰
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