Multi-UAV Cooperative Motion Planning Under Global Spatio-Temporal Path Inspiration in Constraint-Rich Dynamic Environments

When applied in building rescue, forest search and other missions, UAV swarms often encounter challenges such as narrow space constraints and dynamic changes of obstacles, which leads to internal conflicts in the swarm or cause system chaos due to avoiding dynamic obstacles during the execution of tasks. These rigorous characteristics of the actual application scenarios and tasks always make global searching difficult to be optimized and local obstacle avoidance into deadlock. To solve the above problems, this paper introduces a hierarchical online collaborative planning framework inspired by global spatio-temporal paths. In order to reduce the solution complexity while dissolving the overall conflict, global rough searching is conducted for initial spatio-temporal guidance path. To overcome dynamic obstacles without affecting the system internal harmony, online conflict-trend-based clustering co-optimization inspired by the initial path is performed to achieve obstacle avoidance while improving scalability. Experimental tests were conducted in simulation environments with narrow pipelines and dynamic obstacles with comparison to current mainstream centralized and distributed methods. The results demonstrate that our work can generate smoother and safer trajectories with higher success rate in the constraint-rich dynamic environments.