A method for planning multirotor Unmanned aerial vehicle flight paths to cover areas using the Ant Colony Optimization metaheuristic

With the widespread adoption of Unmanned Aerial Vehicles (UAVs) due to increased accessibility to this technology, there has been a growing emphasis on research related to flight path planning, especially for agricultural environments and photogrammetry tasks. UAVs provide several advantages, including crop monitoring and mapping, precise application of inputs, minimal environmental impact, access to challenging areas, and rapid problem response. To efficiently cover large areas, it is crucial to optimize the UAV's resources by considering factors such as terrain topology, mission type, flight autonomy, battery level, UAV inclination, flight altitude, and wind direction. The objective is to introduce a Coverage Path Planning (CPP) method tailored for multirotor UAV. The developed method addresses scenarios with diverse terrains and aims to propose an optimized path by considering the order of terrain visitation. The optimization goals include minimizing the number of turns and the total distance traveled. The method comprises three primary steps. The first step involves the decomposition of areas, where terrains represented by concave polygons are broken down into smaller convex-shaped subareas using a greedy algorithm. The second step calculates the flight direction that minimizes the number of course changes in each subarea. This involves determining the direction aligned with the longest dimension of the polygon to guide the round-trip movement pattern application, commonly known in the literature as boustrophedon. The third step focuses on optimizing the visitation order of the subareas. At this stage, the scenario is modeled as a variant of the Generalized Traveling Salesman Problem (GTSP). To solve this problem, the Ant Colony Optimization algorithm (ACO) is employed. The results obtained from the proposed method are compared with solutions provided by a path planning program already in use in the market, specifically Mission Planner. The results enable the identification of scenarios where the developed method can complement existing market solutions.