多目标优化最低代价无人机机巢选址方法研究

无人机巡检作业中，因为功能与续航距离不同，常面临异构无人机协同和机巢选址问题。无人机机巢的最优部署位置策略，可以看作新的选址优化问题，相对于传统设施选址问题，无人机机巢部署问题面临更多新挑战。笔者综合运用地理信息系统、优劣解距离法对候选点位做预筛选后使用贪心算法和拉格朗日松弛优化的p-中值覆盖问题优化方法，在综合考虑布点原则、飞行任务、飞行半径、功能性冗余等目标因素，提出一种多目标优化最低代价的无人机机巢选址法，将机巢分布问题定义为限制因素预选址前提下的p-中值最低代价问题，设置原则性约束，实现多目标优化最低代价的机巢布点，从多个角度考虑降低巡检成本。实验结果表明：多目标优化后机巢布点在建造、维护、巡检和综合成本上比传统选点方法有9.2%以上的成本节约。

A UAV nest deployment method with multi-target optimization and minimum cost

In the unmanned aerial vehicle (UAV) inspection operation, heterogeneous UAVs often face coordination and nest site selection problems due to their different functions and range capabilities. The optimal deployment strategy of the UAV nest can be seen as a new type of location optimization problem. Compared with the traditional facility location problem, the deployment of the UAV nest is facing more new challenges. This paper comprehensively uses geographic information systems and TOPSIS method to pre-screen candidate locations, and then uses a combination of greedy algorithms and Lagrange relaxation optimization of the p-median coverage problem optimization method. After comprehensively considering factors such as node placement principles, flying tasks, flying radius, and functional redundancy, a multi-objective optimization lowest-cost UAV nest location method is proposed. The nest distribution problem is defined as a p-median problem with the lowest cost under pre-selected restricted factors, and principal constraints are set to achieve multi-objective optimization lowest-cost node placement and reduce inspection costs from multiple perspectives. The experimental results show that the cost savings of the nested distribution after multi-objective optimization are more than 9.2% compared with those of traditional methods in terms of construction, maintenance, inspection, and comprehensive costs.