Title: CSE-ET: Joint Optimization for 3D Multi-UAV Explore-and-Track under Connectivity, Separation, and Energy Constraints
Primary area: applications to robotics, autonomy, planning
Keywords: Coordinated track, multi-agent reinforcement learning, power efficient, exploration, unmanned aerial vehicles (UAVs).

Abstract: We study cooperative target tracking by UAV swarms in open airspace under realistic resource and safety constraints (limited communication, limited power, and inter-UAV safety separation), and introduce active exploration based on spatial information entropy. Most existing methods focus on one or two subtasks (e.g., only tracking accuracy, or treating exploration/communication/power as independent modules), and their metrics and scenario coverage are limited, making it difficult to reflect practical performance under the above constraints. Targeting an evading UAV with an optimal avoidance policy, this paper jointly considers communication, spatial exploration, power consumption, and inter-UAV collision avoidance for the tracking team, and designs a unified joint-optimization framework based on multi-agent reinforcement learning. The framework models low-level action control with a base model, high-level behavior control with a multi-agent decision model, and is updated by a joint optimization algorithm. In simulation, we construct evaluation metrics covering tracking, power, and exploration, and compare with the mainstream multi-agent baselines. Experiments show that our method outperforms the baselines on average by $\textbf{+23\\%}$ in tracking success rate, $\textbf{+25\\%}$ in power saving, $\textbf{+24\\%}$ in spatial exploration, and $\textbf{+18\\%}$ in aggregate reward. To our knowledge, this paper presents the 3D multi-UAV cooperative tracking framework that is most closely aligned with practical constraints.

TL;DR: Joint Optimization for 3D Multi-UAV Explore-and-Track under Connectivity, Separation, and Energy Constraints