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 study the problem of persistent monitoring of a finite number ofinter-connected geographical nodes by a group of heterogeneous mobile agents.We assign to each geographical node a concave and increasing reward functionthat resets to zero after an agent's visit. Then, we design the optimaldispatch policy of which nodes to visit at what time and by what agent byfinding a policy set that maximizes a utility that is defined as the totalreward collected at visit times. We show that this optimization problem isNP-hard and its computational complexity increases exponentially with thenumber of the agents and the length of the mission horizon. By showing that theutility function is a monotone increasing and submodular set function ofagents' policy, we proceed to propose a suboptimal dispatch policy design witha known optimality gap. To reduce the time complexity of constructing thefeasible search set and also to induce robustness to changes in the operationalfactors, we perform our suboptimal policy design in a receding horizon fashion.Then, to compensate for the shortsightedness of the receding horizon approachfor reward distribution beyond the feasible policies of the agents over thereceding horizon, we add a new term to our utility, which provides a measure ofnodal importance beyond the receding horizon's sight. This term gives thepolicy design an intuition to steer the agents towards the nodes with higherrewards on the patrolling graph. Finally, we discuss how our proposed algorithmcan be implemented in a decentralized manner. A simulation study demonstratesour results.

A sub-modular receding horizon solution for mobile multi-agent persistent monitoring