Hierarchical Cooperative Trajectory Planning for Air–Ground Robotic Systems in Communication-Constrained Urban Canyons

Heterogeneous airground robotic systems, which integrate unmanned ground vehicles and unmanned aerial vehicles, have shown significant potential in complex autonomous missions. However, when deployed in urban canyons, dense high-rise buildings impose severe communication constraints on ground vehicles, necessitating the introduction of aerial vehicles as relays to maintain reliable connectivity. The resulting cooperative trajectory planning problem is challenging for three reasons. First, the kinematic and communication constraints are tightly coupled. Second, the optimization landscape is highly non-convex and non-differentiable. Third, the planner must balance topological exploration with real-time efficiency. To address these challenges, we propose a hierarchical cooperative trajectory planning framework for an air–ground robotic system. Specifically, in the upper layer, a heuristic-search-guided reinforcement learning mechanism is employed to narrow the search space and circumvent the sparse reward problem, rapidly generating an initial solution. Subsequently, the lower-layer planner utilizes an optimization-based solver, together with a corridor-based constraint formulation method, to refine the initial solution into a kinematically feasible cooperative trajectory. Ultimately, this strategy improves real-time efficiency while improving the quality of feasible cooperative trajectories. Extensive ablation studies and comparative experiments with representative baselines demonstrate that the proposed framework improves collision avoidance, communication reliability, trajectory smoothness, and computational efficiency in the tested urban canyon scenarios.