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 propose a centralized control framework to select suitable robots from aheterogeneous pool and place them at appropriate locations to monitor a regionfor events of interest. In the event of a robot failure, the frameworkrepositions robots in a user-defined local neighborhood of the failed robot tocompensate for the coverage loss. The central controller augments the team withadditional robots from the robot pool when simply repositioning robots fails toattain a user-specified level of desired coverage. The size of the localneighborhood around the failed robot and the desired coverage over the regionare two objectives that can be manipulated to achieve a user-specified balance.We investigate the trade-off between the coverage compensation achieved throughlocal repositioning and the computation required to plan the new robotlocations. We also study the relationship between the size of the localneighborhood and the number of additional robots added to the team for a givenuser-specified level of desired coverage. We use extensive simulations and anexperiment with a team of seven quadrotors to verify the effectiveness of ourframework. Additionally, we show that to reach a high level of coverage in aneighborhood with a large robot population, it is more efficient to enlarge theneighborhood size, instead of adding additional robots and repositioning them.

Resilient Coverage: Exploring the Local-to-Global Trade-off