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

This paper presents a novel approach that allows a swarm of heterogeneousrobots to produce simultaneously segregative and flocking behaviors using onlylocal sensing. These behaviors have been widely studied in swarm robotics andtheir combination allows the execution of several complex tasks, ranging fromsurveillance and reconnaissance, to search and rescue, to transport, and toforaging. Although there are several works in the literature proposingdifferent strategies to achieve these behaviors, to the best of our knowledge,this paper is the first to propose an algorithm that emerges simultaneouslybehaviors and do not rely on global information or communication. Our approachconsists of modeling the swarm as a Gibbs Random Field (GRF) and usingappropriate potential functions to reach segregation, cohesion and consensus onthe velocity of the swarm. Simulations and proof-of-concept experiments usingreal robots are presented to evaluate the performance of our methodology incomparison to some of the state-of-the-art works that tackle segregativebehaviors.

Flocking-Segregative Swarming Behaviors using Gibbs Random Fields