通过帧间相关性学习动态目标的平滑和鲁棒空间机械臂操作
Learning Smooth and Robust Space Robotic Manipulation of Dynamic Target via Inter-frame Correlation
AI总结：
本文提出一种数据驱动的空间机械臂操作方法，通过利用历史帧与当前帧的时序相关性，实现对未知和非结构化空间环境中动态目标的精确操控。
摘要：
在未来的航天工程中，轨道服务代表了一个关键前沿，动态非合作目标的操作是关键技术。在微重力环境下，物体通常自由漂浮，缺乏地球上的支撑和摩擦约束，显著增加了空间机械臂操作任务的复杂性。传统规划和控制方法主要局限于已知的静态场景，缺乏实时响应能力。为实现对未知和非结构化空间环境中动态目标的精确机械臂操作，本文提出了一种数据驱动的空间机械臂操作方法，该方法整合了历史时间信息和帧间相关性机制。通过利用历史帧与当前帧之间的时序相关性，系统可以有效捕捉场景中的运动特征，从而产生稳定且平滑的动态目标操作轨迹。为验证所提方法的有效性，我们开发了一个地面实验平台，包括一个PIPER X机械臂和双轴线性平台，该平台准确模拟了微重力环境下自由漂浮运动的二维平面。
英文摘要：
On-orbit servicing represents a critical frontier in future aerospace engineering, with the manipulation of dynamic non-cooperative targets serving as a key technology. In microgravity environments, objects are typically free-floating, lacking the support and frictional constraints found on Earth, which significantly escalates the complexity of tasks involving space robotic manipulation. Conventional planning and control-based methods are primarily limited to known, static scenarios and lack real-time responsiveness. To achieve precise robotic manipulation of dynamic targets in unknown and unstructured space environments, this letter proposes a data-driven space robotic manipulation approach that integrates historical temporal information and inter-frame correlation mechanisms. By exploiting the temporal correlation between historical and current frames, the system can effectively capture motion features within the scene, thereby producing stable and smooth manipulation trajectories for dynamic targets. To validate the effectiveness of the proposed method, we developed a ground-based experimental platform consisting of a PIPER X robotic arm and a dual-axis linear stage, which accurately simulates micro-gravity free-floating motion in a 2D plane.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
