无人机已广泛用于各个领域，具有安全系数小和低成本的优势。然而，由于无人机续航时间有限，无法到达较远的距离，一种新的解决方案是利用车辆来携带和发射无人机，即无人机与车辆协同方式，以完成整个的总时间最小为目标。该论文提出了一种两阶段算法来解决无人机与车辆协同作业的路径规划问题。在第一阶段中，利用聚类算法对目标区域进行划分，以确定车辆的分配情况。在第二阶段中，设计并改进了传统的教学优化算法HTLBO，以提高搜索效率，在每个目标区域里求得无人机的行进航线，确保航线的优化性。最后，通过与其他对比算法进行实验结果比较表明，车辆与无人机联合作业模型及HTLBO算法的可行性与鲁棒性，对各个大规模区域下的复杂动态提供了一些思路和参考。

Unmanned aerial vehicles (UAVs) have been widely used in various fields and have the advantages of low safety factor and low cost. However, due to the limited endurance of the UAV, it is impossible to reach a long distance, a new solution is to use the vehicle to carry and launch the UAV, that is, the collaborative way of the UAV and the vehicle, in order to complete the total time of the minimum as the goal. In this paper, a two-stage algorithm is proposed to solve the path planning problem of UAV-vehicle cooperative operation. In the first stage, the target area is divided using a clustering algorithm to determine the distribution of vehicles. In the second stage, the traditional teaching optimization algorithm HTLBO is designed and improved to improve the search efficiency, and the route of the UAV is obtained in each target area to ensure the optimization of the route. Finally, the experimental results compared with other comparison algorithms show that the vehicle-UAV joint operation model and HTLBO algorithm are feasible and robust, and provide some ideas and references for complex dynamics in various large-scale areas.

港珠澳大桥智能化运维技术集成应用