This paper presents a new swarm intelligence-based approach to deal with the cooperative path planning problem of unmanned aerial vehicles (UAVs), which is essential for the automatic inspection of infrastructure. The approach uses a 3D model of the structure to generate viewpoints for the UAVs. The calculation of the viewpoints considers the constraints related to the UAV formation model, camera parameters, and requirements for data post-processing. The viewpoints are then used as input to formulate the path planning as an extended traveling salesman problem and the definition of a new cost function. Ant colony optimization is finally used to solve the problem to yield optimal inspection paths. Experiments with 3D models of real structures have been conducted to evaluate the performance of the proposed approach. The results show that our system is not only capable of generating feasible inspection paths for UAVs but also reducing the path length by 29.47\% for complex structures when compared with another heuristic approach. The source code of the algorithm can be found at https://github.com/duynamrcv/aco_3d_ipp.

翻译：本文提出了一种新的群体智能方法，用于解决无人机（UAV）协作路径规划问题，该问题对基础设施的自动检测至关重要。该方法利用结构的3D模型生成无人机的观测点。观测点的计算考虑了无人机编队模型、相机参数以及数据后处理需求等约束条件。随后，这些观测点被用作输入，将路径规划问题转化为扩展的旅行商问题，并定义了新的成本函数。最终采用蚁群优化算法求解该问题，以获得最优检测路径。利用真实结构的3D模型进行了实验，以评估所提方法的性能。结果表明，与另一种启发式方法相比，本系统不仅能生成可行的无人机检测路径，还能将复杂结构的路径长度缩短29.47%。算法源代码可在https://github.com/duynamrcv/aco_3d_ipp获取。