Unmanned aerial vehicle takeoff point search algorithm with information sharing strategy of random trees for multi-area coverage task

This study proposes a novel approach to optimize full-coverage search in distributed task areas using a single Unmanned Ground Vehicle (UGV) to deliver an Unmanned Aerial Vehicle (UAV) to the takeoff points of each task area along the shortest possible path. Unlike the traditional Traveling Salesman Problem (TSP), task areas are not fixed nodes, and obstacles must be considered. To address these challenges, a probability-based Rapid-exploration Random Tree (p-RRT) with an information-sharing strategy is introduced, significantly improving the efficiency of locating takeoff points in complex environments. A dual optimization method further reduces the number of nodes and path length planned by the D* algorithm, achieving up to an 80 % reduction in nodes and improving path efficiency. Additionally, a simulated annealing (SA) algorithm optimizes the connection sequence of takeoff points, reducing total path length by 35.05 % compared to the initial path and 22.66 % compared to the traditional Random Sampling Method (RSM). Experiments confirm that the proposed algorithms can effectively enhance UGV-UAV collaboration with reducing path complexity and improving energy efficiency, and thus streamline multi-area coverage tasks.