Distributed Real-Time Trajectory Planning for Multiple UAVs in Complex Unknown Environments

Abstract

Challenges in trajectory planning are encountered by fixed-wing unmanned aerial vehicle (UAV) swarms operating in environments with unknown obstacles. In this study, a distributed real-time trajectory-planning method that integrates a distributed model predictive control (DMPC) framework with an adaptive Gaussian collocation strategy (DA-GCMPC) was developed. This method leverages a distributed iterative computational framework based on DMPC to reformulate trajectory planning as an optimal control problem. To address the fixed-resolution limitation of conventional distributed MPC formulations, a complexity-aware adaptive collocation mechanism is introduced. The novelty of the method lies in adapting the collocation transcription resolution of each local MPC problem according to the instantaneous planning complexity. This mechanism selects the collocation type online according to maneuvering demand, obstacle density risk, and neighboring-UAV interaction risk, enabling the planner to balance real-time computation and constraint-handling capability under limited perception. We decomposed the UAV energy consumption and formulated the total energy consumption of the swarm as the objective function. An optimal control sequence was derived using the Gaussian collocation method by integrating obstacle avoidance constraints for fixed-wing UAVs and environmental limitations. Comparative simulations against the implemented fixed-discretization interior-point and SQP baselines showed that the proposed DA-GCMPC method achieved lower computation time and better trajectory quality metrics under the tested simulation settings, with average per-step computation times below 80 ms. In addition, an eight-UAV semi-physical hardware-in-the-loop validation was conducted to verify the real-time executability of the proposed method in a closed-loop flight control system.