Collision-Free Trajectory Generation and Robust Nonlinear Distributed Model Predictive Control for Tethered Multi-Rotor Uncrewed Aerial Vehicles

This article investigates the collaborative transportation planning and control of tethered multi-rotor unmanned aerial vehicles within intelligent transportation systems. These unmanned aerial vehicles handle heavy-load delivery, including emergency airdrop and aerial assembly of structural components. To ensure algorithm generality, obstacles, loads, and unmanned aerial vehicles are modeled as unions of convex sets. Collision avoidance constraints, originally nondifferentiable due to convex set distances, are exactly reformulated into differentiable forms via strong duality. This leads to a smooth, optimization-based trajectory planning framework with obstacle avoidance. Considering composite disturbances, a robust nonlinear distributed model predictive control strategy based on constraint tightening is developed, ensuring robust feasibility and stability without terminal constraints. Numerical simulations in cluttered environments validate the method’s effectiveness and applicability to next-generation aerial logistics and emergency response in complex terrains.