UAV path planning based on K-means algorithm and simulated annealing algorithm

This paper proposed a new method for unmanned aerial vehicle (UAV) path planning based on K-means algorithm and simulated annealing (SA) algorithm, which solves the problem for multi-UAVs with multi-mission under complicated constraints. Firstly, the model is established for the no-fly zone, the target zone and the valid zone for cruise within it in the mission area. Then, the decomposition technique decomposes the valid area into multiple sub-target points reasonably. Secondly, the K-means algorithm is used to cluster the target points of UAV cruise, which solves the problem for UAV cruise range and scheduling issues. Combining the SA algorithm for the similar sub-target route planning, this technique increases the coverage of the UAVs in the sub-target area of cruise valid area. Finally, taking the real data of UAVs in earthquake relief as an example, the effectiveness and robustness of the proposed method is validated by simulation experiments.