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 a pursuit-evasion problem with a heterogeneous team of multiplepursuers and multiple evaders. Although both the pursuers (robots) and theevaders are aware of each others' control and assignment strategies, they donot have exact information about the other type of agents' location or action.Using only noisy on-board sensors the pursuers (or evaders) make probabilisticestimation of positions of the evaders (or pursuers). Each type of agent useMarkov localization to update the probability distribution of the other type. Asearch-based control strategy is developed for the pursuers that intrinsicallytakes the probability distribution of the evaders into account. Pursuers areassigned using an assignment algorithm that takes redundancy (i.e., an excessin the number of pursuers than the number of evaders) into account, such thatthe total or maximum estimated time to capture the evaders is minimized. Inthis respect we assume the pursuers to have clear advantage over the evaders.However, the objective of this work is to use assignment strategies thatminimize the capture time. This assignment strategy is based on a modifiedHungarian algorithm as well as a novel algorithm for determining assignment ofredundant pursuers. The evaders, in order to effectively avoid the pursuers,predict the assignment based on their probabilistic knowledge of the pursuersand use a control strategy to actively move away from those pursues. Ourexperimental evaluation shows that the redundant assignment algorithm performsbetter than an alternative nearest-neighbor based assignment algorithm.

Pursuer Assignment and Control Strategies in Multi-agent Pursuit-Evasion Under Uncertainties