# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
###### Abstract  
Efficient UAV path-planning algorithms significantly improve inspection efficiency and reduce costs. However, due to the limitation of battery capacity, the endurance of existing UAVs is limited, making it difficult for them to directly undertake information collection, cruising, and inspection tasks over large work areas. This paper considers the problem of path allocation for multiple UAVs to minimize work time and reports research on multi-UAV (unmanned aerial vehicle) multi-task long-duration operation path planning. We propose a multi-UAV collaborative search algorithm based on the greedy algorithm (MUCS-GD) and a multi-UAV collaborative search algorithm based on the binary search algorithm (MUCS-BSAE), and later apply two UAV collaborative search algorithms to five UAV flight paths: (1) a snake curve path, (2) a \"square wave signal\" curve path, (3) a Peano curve path, (4) a Hilbert curve path, and (5) a Moore curve path, and compare the simulation results. We found that the performance of MUCS-BSAE was better than that of MUCS-GD in all of the above flight paths. In addition, the path with the \"square wave signal\" curve was the near-optimal path among all the flight paths. Finally, we improved the MUCS-BSAE applied on the \"square wave signal\" curve path and obtained an improved collaborative search algorithm for multiple UAVs based on the binary search algorithm (IMUCS-BSAE), which further reduced the working time of the UAV. collaborative unmanned aerial vehicle teams; coverage path planning (CPP); path planning; multiple traveling salesmen problems (MTSP); predictive task assignment; task allocation; unmanned aerial vehicles (UAV) +
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the introduction of unmanned loaders to act as a power supply base. The loading vehicle is used to carry the UAV to the vicinity of the target node, and then the UAV is dispatched to perform the mission. This strategy can save power for UAVs to cover a wider area. In many similar applications, the use of UAVs is closely related to the problem of CPP (coverage path planning) [11]. This problem relates to ensuring that every area in the environment is covered by the flight path of the UAV--CPP provides a solution for guiding the path planning of the UAV. Due to the limitations of a single UAV in terms of flight distance, large-area surveillance, and search capabilities, UAV swarms composed of multiple UAVs have become increasingly valuable in UAV applications [12]. Compared to individual UAVs, UAV swarms offer significant size and coordination advantages that can increase the effectiveness and reliability of mission accomplishment [13]. The first problem in implementing highly collaborative UAV swarms is planning of the paths of the UAV swarms in a scientific and reasonable way. There have been many studies on single UAV path planning, but there are relatively few studies on path planning for UAV swarms. More and more research activity is shifting from path planning for single UAVs to path planning for swarms of UAVs. Unlike path planning for a single UAV, path planning for a UAV swarm needs to consider not only the flight of a single UAV, but also the number of UAVs in the swarm, path allocation, cooperation methods, etc., which is essentially a multi-objective optimization problem with complex constraints [14; 15; 16]. An intelligent optimization algorithm is extensively used to solve path planning problems because of its less demanding properties and high robustness in optimizing the problem. The multiple UAV path planning problem for visiting multiple target nodes is, in essence, similar to the MTSP (multiple traveling salesman problem). Assigning target nodes to multiple UAVs is a complex permutation and combination problem. It can be solved by exhaustive or heuristic algorithms. The exhaustive method involves searching for the optimal solution by arranging and combining all possible results. When the MTSP problem is small in size, it can be solved using exact algorithms, such as branch and bound algorithms.