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

This paper addresses multi-robot informative path planning (IPP) forenvironmental monitoring. The problem involves determining informative regionsin the environment that should be visited by robots in order to gather the mostamount of information about the environment. We propose an efficient sparseGaussian process-based approach that uses gradient descent to optimize paths incontinuous environments. Our approach efficiently scales to both spatially andspatio-temporally correlated environments. Moreover, our approach cansimultaneously optimize the informative paths while accounting for routingconstraints, such as a distance budget and limits on the robot's velocity andacceleration. Our approach can be used for IPP with both discrete andcontinuous sensing robots, with point and non-point field-of-view sensingshapes, and for both single and multi-robot IPP. We demonstrate that theproposed approach is fast and accurate on real-world data.

Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes