鲁棒主动感知控制用于无全局状态空地协同

中文摘要 AI 辅助

空地协同需要实时的无人机-无人车相对状态信息。与其维持对两个机器人的全局估计，在无人车附着的非惯性系中直接控制可避免对全局定位的依赖。基于被动标记的视觉相对位姿估计提供了一种低成本且有效的解决方案。然而，当所需的无人机姿态与视场约束冲突时，固定相机可能丢失移动无人车的视野。为解决此问题，我们提出COPA，一种用于无全局状态空地协同的鲁棒主动感知框架。我们使用单轴云台将相机光轴与无人机俯仰姿态解耦。我们推导了一个主动感知模型，将无人机运动、云台角度和无人车运动与目标图像平面状态相关联。时间卷积网络根据近期运动历史预测短时域无人车加速度和角速度，无需全局状态测量。模型预测控制利用这些预测联合优化无人机和云台控制。仿真表明COPA维持连续目标可见性，而消融研究确认TCN在无人车运动过渡期间减少峰值误差。在无人车加速度高达3m/s^2和偏航率高达1.0rad/s的真实世界实验中，展示了鲁棒跟踪。

英文摘要

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.

发表机构

• Zhejiang University（浙江大学）
• Huzhou Institute of Zhejiang University（浙江大学湖州研究院）
• The Chinese University of Hong Kong（香港中文大学）

机构由 AI 辅助整理，请以论文原文为准。