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 this https URL

本文提出了一种基于群体智能的方法，用于解决无人机的合作路径规划问题，该问题对于基础设施的自动检查至关重要。该方法使用结构的三维模型生成无人机的视点，并考虑无人机编队模型、相机参数和数据后处理要求的约束，计算视点。然后，将视点作为输入，将路径规划作为扩展的旅行推销员问题，并定义一个新的成本函数。最后使用蚁群优化来解决这个问题，得到最优的检查路径。对真实结构的 3D 模型进行了实验以评估所提出方法的性能。结果表明，与另一种启发式方法相比，我们的系统不仅能够为无人机生成可行的检查路径，而且在复杂结构中可以将路径长度减少 29.47%。该算法的源代码可以在此 https 的 URL 找到。