基于时空解耦规划方法的无人机自主降落策略

In air-ground collaborative systems, the coordinated landing of unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) is of paramount importance for extending the task scenarios of heterogeneous intelligent agent clusters. Current trajectory optimization-based autonomous landing methods couple the temporal and spatial dimensions by designing the optimal control laws for joint trajectory optimization. However,the optimization objective function design is relatively complex,and it cannot fully utilize the actuator’s optimal performance. A novel spatio-temporal decomposition planning (STDP) method is proposed to address the excessive coupling of time and space in traditional trajectory optimization methods. The STDP method optimizes the landing trajectories separately in spatial and temporal dimensions, enabling UAVs to adopt more aggressive flight strategies in complex scenarios. Furthermore,the objective function is meticulously designed to account for the UAV’s landing time and motor power consumption model,formulating a second-order cone programming problem to expedite the solution process while ensuring high-quality and efficient solutions. Simulated results indicate that, compared to spatio-temporal coupled planning methods,the STDP method generates the trajectories that closely adhere to kinematic constraints,substantially reducing the task completion time and enhancing the mission efficiency. Additionally, the empirical tests in real-world scenarios confirm the reliability and efficacy of STDP method in practical application.