随着飞行任务的难度、危险性和强度的不断增加，无人机以其灵活性、便携性和较强的隐蔽性得到了广泛的应用，并发挥着越来越重要的作用。无人机完成任务的前提是要规划一条从起点到终点的路径，既要能够避开禁飞区和其他威胁源，又要满足飞机的动力学约束和其他约束。多无人机协同任务已经成为无人机发展的必然趋势，把复杂的任务分解成多个小任务，并分配给多架无人机执行，可以提高效率，增强任务执行能力。多无人机路径规划是一个多目标优化问题，既要满足可飞行性、避碰性的要求，又要保证协同任务的完成。群体智能算法是近年来兴起解决组合优化问题的有效方法，本文围绕群体智能多无人机路径规划展开研究，针对不同场景下路径规划面临的实际问题，从全局路径规划、局部路径规划及任务分配进行深入研究。主要研究内容和成果如下：

（1）静态环境中，针对应用人工蜂群算法解决全局路径规划时容易陷入局部最优、收敛速度慢的问题，提出一种改进的人工蜂群算法。该算法对标准算法进行了两点改进：首先，考虑到混沌的遍历性和随机性，提出一种改进的初始化策略，利用Tent混沌映射方法初始化种群以避免陷入局部最优；其次，通过应用全局最优解指导雇佣蜂阶段新候选解的搜索，实现对全局的有导向粗搜索，应用Logistic混沌映射指导旁观蜂阶段新候选解的搜索，实现局部的精搜索，从而提高收敛速度。此外，为了符合无人机的运动学模型，对规划出的路径点进行平滑处理。

（2）动态环境中，针对应用动态窗口算法解决局部路径规划时存在的全局规划路径能力差、规划过程拐角尖峰多以及不能应对密集障碍物环境等问题，提出一种融合算法。首先，利用全局路径规划的关键点作为动态窗口算法的子目标点，通过优化航行过程的位姿以及自适应调整评价函数权重来改进动态窗口算法，可有效提高全局路径规划能力。然后，通过融合改进人工蜂群算法和改进动态窗口算法实现实时路径规划，提高路径跟踪精度，提高时效性，可有效应对密集障碍物环境。

（3）针对任务数和无人机不匹配情况下的任务分配问题，设计了一种任务分配模型，分别应用典型群体智能算法中的遗传算法、蚁群算法以及粒子群算法求解，并从奖励值和运行时间两个方面对比评估相关算法的性能，选用性能较优的遗传算法实现任务分配。最后，实验验证了多无人机在不同场景的路径规划的合理性和有效性。

本文提出的改进方法，有效提升了不同场景下多无人机路径规划的综合性能，对今后多无人机路径规划研究提供参考。

With the increasing difficulty, danger and intensity of flight missions, unmanned aerial vehicles (UAVs) have been widely used due to their flexibility, portability and strong concealment, and are playing an increasingly important role. The premise for a UAV to complete a mission is to plan a path from the start point to the end point, which must be able to avoid no-fly zones and other threats, but also satisfy the dynamic constraints and other constraints of the aircraft. Multi-UAV collaborative tasks have become an inevitable trend in the development of UAVs. Decomposing complex tasks into multiple small tasks and assigning them to multiple UAVs can improve efficiency and enhance task execution capabilities. Multi-UAV path planning is a multi-objective optimization problem, which must not only meet the requirements of flightability and collision avoidance, but also ensure the completion of collaborative tasks. Swarm intelligence algorithm is an effective method to solve combinatorial optimization problems in recent years. This paper focuses on the research of swarm intelligent multi-UAV path planning. Aiming at the actual problems faced by path planning in different scenarios, the global path planning, local path planning and task assignment Do in-depth research. The main research contents and results are as follows:

 

(1) In the static environment, aiming at the problem that the artificial bee colony algorithm is easy to fall into the local optimum and the convergence speed is slow when solving the global path planning, an improved artificial bee colony algorithm is proposed. This algorithm has made two improvements to the standard algorithm: first, considering the ergodicity and randomness of chaos, an improved initialization strategy is proposed, using the Tent chaotic mapping method to initialize the population to avoid falling into local optimum; second, by applying the global The optimal solution guides the search of new candidate solutions in the hired bee stage to achieve a global oriented coarse search, and the Logistic chaotic map is used to guide the search for new candidate solutions in the bystander bee stage to achieve local fine search, thereby improving the convergence speed. In addition, in order to conform to the kinematic model of the UAV, the planned waypoints are smoothed.

 

(2) In the dynamic environment, a fusion algorithm is proposed to solve the problems of poor global planning path ability, many corner peaks in the planning process, and inability to deal with dense obstacle environments when the dynamic window algorithm is used to solve the local path planning. First, the key points of the global path planning are used as the sub-target points of the dynamic window algorithm, and the dynamic window algorithm is improved by optimizing the pose of the navigation process and adaptively adjusting the weight of the evaluation function, which can effectively improve the global path planning ability. Then, by integrating the improved artificial bee colony algorithm and the improved dynamic window algorithm to realize real-time path planning, improve path tracking accuracy, improve timeliness, and effectively deal with dense obstacle environments.

 

(3) Aiming at the problem of task allocation when the number of tasks does not match the UAV, a task allocation model is designed, which is solved by applying the genetic algorithm, ant colony algorithm and particle swarm algorithm in typical swarm intelligence algorithms respectively, and from the reward The performance of related algorithms is compared and evaluated in terms of value and running time, and the genetic algorithm with better performance is selected to realize task allocation. Finally, experiments verify the rationality and effectiveness of path planning for multiple UAVs in different scenarios.

 

The improved method proposed in this paper effectively improves the comprehensive performance of multi-UAV path planning in different scenarios, and provides a reference for future research on multi-UAV path planning.

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