Trajectory planning of unmanned aerial vehicle based on adaptive ant colony algorithm

In order to solve the problem of unmanned aerial vehicle trajectory planning, an adaptive ant colony algorithm is proposed, which is different from the standard ant colony algorithm, and the local search mode is adopted in this algorithm. Firstly, according to the relative position relation between the starting node and the target node, the corresponding search pattern is selected, then the transfer probability of each node is calculated, and the next node is selected according to the roulette rules. The simulation results show that the adaptive ant colony algorithm has the advantages of fewer search nodes, faster speed and so on, while reducing the track cost and reducing the computational time. In addition, the adaptive ant colony algorithm can avoid the emergence of singular track segments, thus guaranteeing the actual flight of the obtained track, which shows that the overall performance of the proposed algorithm is significantly superior to the standard ant colony algorithm.

This data (unmanned aerial vehicle trajectory planning based on adaptive ant Colony algorithm) is reproduced in a cattle net
Materials need to download themselves, you can go to the forum to download

Trajectory planning of unmanned aerial vehicle based on adaptive ant colony algorithm