Hierarchical task allocation and chance-constrained trajectory optimization for heterogeneous quadrotor swarms

This paper addresses the multi-task cooperative planning problem of speed-heterogeneous quadrotor UAV swarms in complex environments and proposes a hierarchical planning framework integrating task allocation and trajectory optimization. The framework aims to maximize task rewards while minimizing execution time under constraints induced by obstacles, high-risk regions, and various uncertainties. At the upper-level task allocation layer, a topology-based clustering and group-level resource matching strategy is developed. Based on task spatiotemporal constraints and heterogeneous UAV capabilities, the strategy performs macroscopic UAV-task subdomain partitioning, followed by fine-grained assignment within each local subdomain using mixed-integer linear programming (MILP). This approach achieves effective task optimization while significantly reducing computational complexity. At the lower-level trajectory generation layer, a time-optimal trajectory optimization model is formulated by considering quadrotor dynamic constraints and environmental risks. Control input uncertainties and terminal state uncertainties are modeled using chance constraints, which are transformed into deterministic constraints via an asymmetric exponential ratio approximation function. This treatment reduces conservatism while ensuring chance constraint satisfaction, thereby improving trajectory feasibility and optimality. Simulation results demonstrate that the proposed framework effectively handles cooperative task planning for heterogeneous UAV swarms, enhancing mission efficiency while ensuring safety under uncertainty.