自主无人机近距管道 inspection 通过扰动感知预测视觉伺服
Autonomous UAV Pipeline Near-proximity Inspection via Disturbance-Aware Predictive Visual Servoing
AI总结：
本文提出基于图像视觉伺服模型预测控制的自主四旋翼近距管道 inspection 框架，通过扩展状态卡尔曼滤波与图像特征预测减少扰动影响，提升复杂地形下的 inspection 精度。
摘要：
可靠的管道 inspection 对安全能源运输至关重要，但受限于距离远、地形复杂和对人类 inspector 的风险。无人驾驶航空器提供了一种灵活的传感平台，但可靠的自主 inspection 仍具挑战性。本文提出基于图像视觉伺服模型预测控制（VMPC）的自主四旋翼近距管道 inspection 框架，用于三维场景。统一的预测模型耦合四旋翼动力学与图像特征运动学，使控制回路中可直接进行图像空间预测。为解决低频视觉更新、测量噪声和环境不确定性，开发了带有图像特征预测的扩展状态卡尔曼滤波方案（ESKF-PRE），并将估计的 lumped 扰动纳入 VMPC 预测模型，得到 ESKF-PRE-VMPC 框架。引入地形自适应速度设计，以在未知地形坡度上维持期望巡航速度并生成垂直速度参考，无需先验地形信息。该框架在高保真度的 Gazebo 模拟和现实实验中得到验证。在现实测试中，所提方法在无风的直管 inspection 中将 RMSE 减少 52.63% 和 75.04%，并在基线方法失败的风扰和弯管任务中成功完成。一个开源的 nano 四旋翼被修改用于室内实验。
英文摘要：
Reliable pipeline inspection is critical to safe energy transportation, but is constrained by long distances, complex terrain, and risks to human inspectors. Unmanned aerial vehicles provide a flexible sensing platform, yet reliable autonomous inspection remains challenging. This paper presents an autonomous quadrotor near-proximity pipeline inspection framework for three-dimensional scenarios based on image-based visual servoing model predictive control (VMPC). A unified predictive model couples quadrotor dynamics with image feature kinematics, enabling direct image-space prediction within the control loop. To address low-rate visual updates, measurement noise, and environmental uncertainties, an extended-state Kalman filtering scheme with image feature prediction (ESKF-PRE) is developed, and the estimated lumped disturbances are incorporated into the VMPC prediction model, yielding the ESKF-PRE-VMPC framework. A terrain-adaptive velocity design is introduced to maintain the desired cruising speed while generating vertical velocity references over unknown terrain slopes without prior terrain information. The framework is validated in high-fidelity Gazebo simulations and real-world experiments. In real-world tests, the proposed method reduces RMSE by 52.63% and 75.04% in pipeline orientation and lateral deviation in the image, respectively, for straight-pipeline inspection without wind, and successfully completes both wind-disturbance and bend-pipeline tasks where baseline method fails. An open-source nano quadrotor is modified for indoor experimentation.
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