VIRGA: Virtual-Agent-Intermediated Riemannian Geometry for Active-Sensing Air-Ground Coordination

2026-09-18

AI Summary

论文提出VIRGA框架，通过虚拟代理和Riemannian几何场解决无人机与地面无人车在动态障碍物环境中的协同感知与控制问题。

Abstract

Air-ground autonomy becomes harder when the unmanned aerial vehicle (UAV) must remain observable by a gimbal light detection and ranging (LiDAR) mounted on the unmanned ground vehicle (UGV). The platforms must avoid dynamic obstacles while coordinating heterogeneous motion, limited sensing, and changing task initiative within one closed loop. This paper presents VIRGA, a neural geometric coordination framework that turns dual-LiDAR observations into bounded source-specific Riemannian fields and couples them through a virtual agent with reciprocal elastic feedback. Platform-aware execution maps convert the shared coordination reference into feasible UAV, UGV, and gimbal commands while enforcing active-observation safeguards. Evaluation against three complementary baselines reveals distinct limitations. An adapted Ray-RMP controller provides the fastest Riemannian response but produces insufficient clearance in the coupled air-ground task. A dense analytical Riemannian field improves geometric avoidance, yet its high evaluation cost prevents stable field-of-view maintenance. An adapted ColAG controller achieves the lowest latency but still incurs safety and observability violations. VIRGA completes all paired warehouse conditions safely, while a long-range cave stress test without retraining demonstrates sustained coordination in irregular and confined geometry. Ablations confirm contributions from online geometric evaluation, virtual-agent mediation, and reciprocal feedback.

Problem

• Air-ground coordination
• Active sensing
• Riemannian geometry
• Dynamic obstacles
• Heterogeneous motion

Innovation

• Virtual-Agent-Intermediated
• Riemannian Geometry
• Active-Sensing
• Air-Ground Coordination
• Dual-LiDAR Observations