English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Space free-flyers like the Astrobee robots currently operating aboard theInternational Space Station must operate with inherent system uncertainties.Parametric uncertainties like mass and moment of inertia are especiallyimportant to quantify in these safety-critical space systems and can change inscenarios such as on-orbit cargo movement, where unknown grappled payloadssignificantly change the system dynamics. Cautiously learning theseuncertainties en route can potentially avoid time- and fuel-consuming puresystem identification maneuvers. Recognizing this, this work proposes RATTLE,an online information-aware motion planning algorithm that explicitly weightsparametric model-learning coupled with real-time replanning capability that cantake advantage of improved system models. The method consists of a two-tiered(global and local) planner, a low-level model predictive controller, and anonline parameter estimator that produces estimates of the robot's inertialproperties for more informed control and replanning on-the-fly; all levels ofthe planning and control feature online update-able models. Simulation resultsof RATTLE for the Astrobee free-flyer grappling an uncertain payload arepresented alongside results of a hardware demonstration showcasing the abilityto explicitly encourage model parametric learning while achieving otherwiseuseful motion.

Online Information-Aware Motion Planning with Inertial Parameter Learning for Robotic Free-Flyers