Low-Altitude Multi-UAV Trajectory Planning in Dynamic Urban Environments Using Dynamic-Aware ACO and MPC-GWO
TL;DR
A hierarchical three-dimensional cooperative trajectory-planning framework integrating dynamic-risk-aware Ant Colony Optimization with cooperative Model Predictive Control–Gray Wolf Optimizer (MPC-GWO) is proposed, demonstrating satisfactory safety, coordination, adaptability, and real-time performance in small- to medium-scale simulated urban scenarios.
Abstract
Low-altitude urban environments pose significant challenges to multi-UAV trajectory planning because of dense buildings, constrained airspace, dynamic obstacles, inter-UAV conflicts, and terminal-area congestion. This study proposes a hierarchical three-dimensional cooperative trajectory-planning framework integrating dynamic-risk-aware Ant Colony Optimization (ACO) with cooperative Model Predictive Control–Gray Wolf Optimizer (MPC-GWO). Environmental costs and predicted dynamic-obstacle risks are incorporated into the ACO global search to generate risk-aware reference trajectories, while a sliding-window GWO improves trajectory smoothness and execution feasibility. During online execution, cooperative MPC-GWO combines dynamic-obstacle prediction, inter-UAV separation constraints, reconfigurable formation switching, and goal-neighborhood safety control to achieve adaptive obstacle avoidance, cooperative replanning, and orderly terminal arrival. Thirty-run Monte Carlo simulations show that the proposed method achieves a success rate of 93.3% ± 25.4% and the highest composite score of 96.20 ± 5.30, with zero dynamic-obstacle and inter-UAV collisions. Ablation experiments verify the effectiveness of the dynamic prediction, formation reconfiguration, and terminal safety-control mechanisms. The average online replanning time remains below 0.5 s, demonstrating satisfactory safety, coordination, adaptability, and real-time performance in small- to medium-scale simulated urban scenarios.