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

We consider the problem of sample-based feedback motion planning frommeasurements affected by systematic errors. Our previous work presented outputfeedback controllers that use measurements from landmarks in the environment tonavigate through a cell-decomposable environment using duality, ControlLyapunov and Barrier Functions (CLF, CBF), and Linear Programming. In thispaper, we build on this previous work with a novel strategy that allows the useof measurements affected by systematic errors in perceived depth (similarly towhat might be generated by vision-based sensors), as opposed to accuratedisplacement measurements. As a result, our new method has the advantage ofproviding more robust performance (with quantitative guarantees) wheninaccurate sensors are used. We test the proposed algorithm in the simulationto evaluate the performance limits of our approach predicted by our theoreticalderivations.

Output-Feedback Path Planning with Robustness to State-Dependent Errors