Agile target capture with UAV swarm in dense environments

Abstract

Rapidly capturing moving targets is critical for maintaining border security and public safety. However, traditional swarm-based target acquisition models often rely on the navigator’s observations and focus on fixed-formation capture methods. When target positions are uncertain and moving rapidly, deadlocks may occur due to target loss. To address this issue, this paper formulates a sliding-window pose graph optimization framework combined with trajectory prediction to estimate and predict the three-dimensional trajectory of agile targets in real time. By adopting a virtual center-of-mass extension strategy to optimize non-uniform circular capture formations, compact target enclosure and obstacle avoidance are achieved. This paper further integrates field-of-view awareness with yaw coordination by formulating a multi-constraint optimization problem. The proposed approach focuses on target-state fusion, prediction, and cooperative capture, demonstrating strong performance in multiple unmanned aerial vehicles (UAVs) cooperative tracking, target visibility maintenance, obstacle avoidance planning, and collision avoidance in dense environments.