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Detecting and counteracting the sabotaging agent in a group of unmanned ground vehicles (UGVs) performing stochastic patrolling task
Author(s): Sergey Korchagin; Evgeny Shvets
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Paper Abstract

Patrolling is a task of providing a uniform coverage of some area with one or several vehicles. Recent scientific developments focus on patrolling using multiple cooperative autonomous agents without a single center of command. Using a group of agents can increase the efficiency of patrolling; however, group algorithms need to govern not only individual movements, but also cooperation between agents and their distribution over the area. To achieve cooperation agents exchange data about their movements and the patrolling area. Agent's decisions can be affected by information received from other agents, however, few studies had considered how the presence of incorrect information affects the patrolling’s efficiency. In this paper we consider a novel problem of counteracting and detecting a sabotaging agent in the context of a multi-agent stochastic patrolling. We consider the modified Social Potential Fields approach, propose a model of sabotaging agent and develop two algorithms for its counteraction and detection.

Paper Details

Date Published: 15 March 2019
PDF: 9 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412W (15 March 2019); doi: 10.1117/12.2522977
Show Author Affiliations
Sergey Korchagin, Institute for Information Transmission Problems (Russian Federation)
Evgeny Shvets, Institute for Information Transmission Problems (Russian Federation)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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