Learning Smooth and Robust Space Robotic Manipulation of Dynamic Target via Inter-frame Correlation

TL;DR

This paper introduces a data-driven approach for space robotic manipulation of dynamic targets, leveraging inter-frame correlation to improve stability and smoothness in microgravity environments.

Contribution

It proposes a novel method that uses temporal correlation and historical data to enable real-time, precise manipulation of moving space objects in unstructured environments.

Findings

Effective exploitation of inter-frame correlation improves manipulation stability.

The approach achieves smooth trajectories in microgravity simulation.

Experimental validation demonstrates practical applicability.

Abstract

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.