Multi-objective evolutionary algorithm with two balancing mechanisms for heterogeneous UAV swarm path planning

Abstract

Unmanned aerial vehicle (UAV) swarm path planning involves creating efficient routes based on task requirements to enable collaborative flight. Compared to homogeneous UAV swarm, the application scenarios of heterogeneous UAV swarm have become increasingly widespread. They can fully leverage the various capabilities of drones and show higher economic benefits. Existing research mainly focuses on homogeneous UAV swarms, and the model for uniformly describing heterogeneous UAV swarm from a functional perspective is insufficient. Differences in dynamic constraints and energy consumption models create challenges for accurately characterizing the path planning problem of heterogeneous UAV swarm. To supplement the above deficiencies, this article designs the scenario and composition structure of heterogeneous UAV swarm. The path-planning problem of heterogeneous UAV swarm is modeled as a multi-objective optimization (MOO) problem, in which a comprehensive energy consumption objective is constructed. To better balance multiple objectives and obtain high-quality solutions, a MOO evolutionary algorithm based on heterogeneous UAV swarm, namely HMOEA, is proposed. Specifically, HMOEA is implemented by combining the proposed two strategies. To verify the model’s feasibility and the algorithm’s effectiveness, numerical simulations and prototype simulations are provided. In numerical simulations, the proposed algorithm was compared with various advanced algorithms, i.e., NSGA-II, CIACO, AP-GWO, CL-DMSPSO, and DSNSGA-III, in two designed terrain problems. The results demonstrate that HMOEA not only outperforms the compared algorithms on convergence and diversity indicators increased over 4% and 2% respectively. Normal flight results were achieved in the two scenarios served by the prototype simulation, namely, urban buildings and forest scenes. Specific implementation and application can be achieved in military or civilian scenarios like reconnaissance and strike missions, search and rescue missions. The proposed model can adapt to more task scenarios, and the proposed method can provide faster and higher quality results for heterogeneous UAV swarm routes. In actual deployment, adjusting model parameters and optimizing the computing environment according to application requirements are worth further investigation to achieve optimal effect.