Obstacle Avoidance Distributed Tracking of Networked UAVs with Online Path Planning
Qiang Zhang, Kaiyu Qin, Bowen Chen, Shiji Tong, Haoyang Yuan, Boxian Lin, Mengji Shi · 2025
This paper addresses the dual challenges of obstacle avoidance and distributed tracking in networked Unmanned Aerial Vehicles (UAVs) by leveraging online planning techniques. Specifically, we introduce a trajectory planning framework for the leader UAV, which is based on the Ego-Planner and utilizes high-performance sensors alongside an advanced onboard computing system. The optimized trajectory of the leader UAV serves as a dynamic reference for the entire UAV network. Each low-cost follower UAV implements an online learning algorithm to extract high-order trajectory information from the leader, enabling the generation of feedforward control components. These feedforward components are integrated into a feedbackfeedforward control architecture, facilitating precise distributed tracking and effective obstacle avoidance. The proposed method significantly enhances cooperative UAV navigation in complex and obstacle-dense environments, demonstrating that low-cost UAVs can achieve reliable performance. Extensive simulations validate the approach's effectiveness, showing superior obstacle avoidance and tracking accuracy compared to conventional methods.