Abstract
Aerial-ground cooperation requires real-time UAV--UGV relative-state information. Instead of maintaining global estimates for both robots, direct control in a UGV-attached non-inertial frame avoids reliance on global localization. Vision-based relative pose estimation with a passive marker offers a low-cost and effective solution. However, a fixed camera may lose sight of the moving UGV when the required UAV attitude conflicts with the field-of-view (FOV) constraint. To address this, we propose COPA, a robust active-perception framework for global-state-free aerial-ground cooperation. We use a single-axis gimbal to decouple the camera optical axis from the UAV pitch attitude. We derive an active-perception model that relates UAV motion, gimbal angle, and UGV motion to the target image-plane state.A Temporal Convolutional Network (TCN) predicts short-horizon UGV acceleration and angular velocity from recent motion history without global-state measurements. The model predictive control (MPC) uses these predictions to jointly optimize UAV and gimbal control. Simulations show that COPA maintains continuous target visibility, while ablation studies confirm that the TCN reduces peak errors during UGV motion transitions. Real-world experiments with UGV accelerations up to 3m/s^2 and yaw rates up to 1.0rad/s demonstrate robust tracking.
Abstract (translated)
URL
https://arxiv.org/abs/2609.25898
