Abstract:

Focusing on the path planning problem for the resolution of high-risk head-on flight conflicts, a UAV resolution path model based on minimum energy consumption was proposed. Multiple factors, such as paths and turns, obstacles, and safety separation, were comprehensively considered in this model. In the model solution, an improved SSA with multi-strategy integration was proposed by improving the chaotic mapping, the golden sine and the follower position update strategy. Its effectiveness was demonstrated by comparing it against 3 other mainstream swarm-intelligence algorithms on 8 standard benchmark functions. For the issue of flight conflict resolution, multi-aircraft conflict scenarios and reduced conflict scenarios were constructed, and 4 algorithms were applied for multiple trials. The comparison was made from two aspects: convergence performance and running time. Simulation results show that, in 4-UAV head-on conflicts scenario, a fitness of 8.86 with a runtime of 4.06 s are achieved by the improved algorithm, while a fitness of 1.26 and a runtime of 3.03 s are obtained in the 3-UAV induced-conflict scenario. All results are optimal, indicating that reasonable resolution paths for head-on conflicts among multiple UAVs can be rapidly provided by the proposed approach. When dealing with complex conflict problems, both computational efficiency and accuracy are taken into account. The new algorithm can quickly plan paths for the resolution of multi-machine head on flight conflicts.

Key words: sparrow search algorithm(SSA), unmanned aerial vehicles(UAV), head-on flight conflicts, conflict resolution, path planning