Cooperative task allocation method for air-sea heterogeneous unmanned system with an application to ocean environment information monitoring

Ocean environment information monitoring is crucial to maritime battlefield situation awareness, marine disaster prevention, and marine resources utilization. However, current monitoring means, including remote sensing satellites, scientific investigation ships, and ocean observation stations, have limitations in terms of spatial coverage, spatiotemporal resolution, and manpower resources. Therefore, this study proposed the Air-Sea heterogeneous unmanned system composed of UAVs and USVs to carry out ocean environment information monitoring tasks.) Firstly, an algorithm named FASO (Firefly Algorithm Based Particle Swarm Optimization) is proposed to obtain the deployment of monitoring nodes in the sea-surface and airspace. Secondly, a multi-objective optimization cooperative task allocation model is established, which contains complex constraints and takes the energy consumption cost of UAVs and USVs as optimization goals. Finally, a cooperative task allocation method based on the improved NSGA-II algorithm is presented to enhance the convergence speed and quality of the cooperative task allocation for air-sea heterogeneous unmanned system. Simulation results demonstrate that the proposed algorithm have higher generality and superiority compared to other algorithms. This study provides a specific approach for the application of heterogeneous unmanned systems in the field of ocean monitoring.