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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present a data-driven optimization approach for robotic controlleddeposition with a degradable tool. Existing methods make the assumption thatthe tool tip is not changing or is replaced frequently. Errors can accumulateover time as the tool wears away and this leads to poor performance in the casewhere the tool degradation is unaccounted for during deposition. In theproposed approach, we utilize visual and force feedback to update the unknownmodel parameters of our tool-tip. Subsequently, we solve a constrained finitetime optimal control problem for tracking a reference deposition profile, whereour robot plans with the learned tool degradation dynamics. We focus on arobotic drawing problem as an illustrative example. Using real-worldexperiments, we show that the error in target vs actual deposition decreaseswhen learned degradation models are used in the control design.

Data-Driven Optimization for Deposition with Degradable Tools