Learning Smooth and Robust Space Robotic Manipulation of Dynamic Target via Inter-frame Correlation
For space robotics engineers, this work addresses the challenge of manipulating unknown dynamic targets in microgravity, offering a data-driven alternative to traditional planning methods.
This paper proposes a data-driven space robotic manipulation method that leverages inter-frame correlation and historical temporal information to achieve smooth and robust manipulation of dynamic non-cooperative targets in microgravity. Experimental results on a ground-based platform show improved trajectory smoothness and stability compared to conventional methods.
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.