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.

Keywords

Swarm control / state estimation / capture strategy / trajectory planning

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