The Path Planning Study of Multi-task Logistics UAVs under Complex Low Airspace

The logistics unmanned aerial vehicles (UAVs) carry out goods to each mission point and return to the starting point, which is a typical TSP problem. However, in real life, logistics UAVs need to complete the TSP problem under the constraints of airspace obstacles. Aiming at this problem, a UAVs path planning algorithm based on the combination of ant colony algorithm and unnecessary point deletion strategy is proposed. First, use ant colony algorithm to complete the path planning without obstacles constraints. Secondly, considering obstacles constraints, obtain the corresponding turning points in the obstacle area, delete unnecessary points and update the turning points and distance. Finally, based on local updates, iterate to obtain global solution. The simulation results show that this method can realize the TSP path planning problem under the condition of multiple obstacles. The planned path is shorter and the convergence speed is faster. In this paper, the TSP problem under obstacle constraints is divided into traditional TSP problems and obstacle avoidance problems, and respectively solved by ant colony algorithm and unnecessary point deletion strategy. In addition, this paper deletes unnecessary points to make the algorithm obtain a shorter path under the premise of ensuring the safety of the path, which overcomes the situation that the ant colony algorithm is easy to fall into the local optimal solution.