Evolutionary Algorithm with Geometrical Heuristics for Solving the Close Enough Traveling Salesman Problem: Application to the Trajectory Planning of an Unmanned Aerial Vehicle

Evolutionary algorithms have been widely studied in the literature to find sub-optimal solutions complex problems as Traveling Salesman Problem (TSP). In such a problem, target positions are usually static and punctually defined. The objective is minimize cost function minimal distance, time or energy. However, some applications, one addressed this paper—namely data collection of buried sensor nodes by means an Unmanned Aerial Vehicle— targets areas with varying sizes: they defined respect radio communication range each node, ranging from few meters several hundred according various parameters (e.g., soil moisture, burial depth, transmit power). Vehicle has enter successively these dynamic collect data, without need pass at vertical node. Some can obviously intersect. That leads solve Close Enough TSP. To determine trajectory for Vehicle, paper presents original efficient strategy based on evolutionary algorithm completed geometrical heuristics. performances highlighted through scenarios respectively 15 50 locations. results analyzed total route length. Finally, conclusions future research directions discussed.