Model Predictive Trajectory Optimization and Control for UAV Replacement in Close Formation Flight With Local Minima Avoidance
Seung‐Mok Lee, Soojung Son · IEEE Access · 2025
This paper proposes a model predictive trajectory optimization and control method for unmanned aerial vehicle (UAV) replacement in close formation flight. When a UAV in close formation fails and needs to be replaced, the replacement UAV must compute a collision-free trajectory to reach the specific relative position previously occupied by the failed UAV. In close formation flight, the replacement UAV must navigate in a densely packed environment, which increases the risk of collision. To reduce this risk, trajectory optimization is performed with constraints that enforce a minimum safe distance between UAVs. However, due to the confined space, the replacement UAV may become trapped in a local minimum during optimization, preventing it from converging to the desired relative position. To address this issue, this paper presents a model predictive trajectory optimization and control method that incorporates a potential field to generate 3D collision-free trajectories that are close to the global optimum for UAV replacement. In addition, a simplified collision avoidance constraint and convergence conditions for UAV replacement are derived. Theoretical analysis demonstrates that the proposed method ensures trajectory convergence while maintaining stability throughout the replacement process. To validate the performance of the proposed method, a hardware-in-the-loop (HITL) test environment was developed based on 3D robot simulator Gazebo and multiple single-board computers to closely replicate real-world hardware tests. The results of the HITL tests show that the proposed method effectively enables UAV replacement in close formation flight in various scenarios.