The RATTLE Motion Planning Algorithm for Robust Online Parametric Model Improvement with On-Orbit Validation

TL;DR

This paper extends the RATTLE motion planning algorithm to enhance real-time, robust, online-updateable planning for robotic systems with parametric uncertainties, validated through microgravity experiments on the ISS.

Contribution

The paper introduces improvements to RATTLE, enabling automatic information content adjustment and robustness guarantees, with real-world validation on space robots.

Findings

RATTLE effectively manages parametric uncertainties in real-time.

Experimental validation on ISS demonstrates practical applicability.

Algorithm achieves a balance between goal achievement and information gathering.

Abstract

Certain forms of uncertainty that robotic systems encounter can be explicitly learned within the context of a known model, like parametric model uncertainties such as mass and moments of inertia. Quantifying such parametric uncertainty is important for more accurate prediction of the system behavior, leading to safe and precise task execution. In tandem, providing a form of robustness guarantee against prevailing uncertainty levels like environmental disturbances and current model knowledge is also desirable. To that end, the authors' previously proposed RATTLE algorithm, a framework for online information-aware motion planning, is outlined and extended to enhance its applicability to real robotic systems. RATTLE provides a clear tradeoff between information-seeking motion and traditional goal-achieving motion and features online-updateable models. Additionally, online-updateable low level control robustness guarantees and a new method for automatic adjustment of information content down to a specified estimation precision is proposed. Results of extensive experimentation in microgravity using the Astrobee robots aboard the International Space Station and practical implementation details are presented, demonstrating RATTLE's capabilities for real-time, robust, online-updateable, and model information-seeking motion planning capabilities under parametric uncertainty.