Recent Advances on Multi-agent Patrolling

Almeida, Alessandro de Luna; Ramalho, Geber; Santana, Hugo; Tedesco, Patrícia; Menezes, Talita Gomes Calaça; Corruble, Vincent; Chevaleyre, Yann

doi:10.1007/978-3-540-28645-5_48

Cited by 108 publications

(103 citation statements)

References 5 publications

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“…A comparative study up to 2004 was presented in [10], which analyzed many different approaches. They observed that the best strategy depends on the topology of the environment and the agents' population size.…”

Section: Alternative Methodsmentioning

confidence: 99%

A Survey on Multi-robot Patrolling Algorithms

Portugal

Rocha

2011

IFIP Advances in Information and Communication Technology

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Abstract. This article presents a survey on cooperative multi-robot patrolling algorithms, which is a recent field of research. Every strategy proposed in the last decade is distinct and is normally based on operational research methods, simple and classic techniques for agent's coordination or alternative, and usually more complex, coordination mechanisms like market-based approaches or reinforcement-learning. The variety of approaches differs in various aspects such as agent type and their decision-making or the coordination and communication mechanisms. Considering the current work concerning the patrolling problem with teams of robots, it is felt that there is still a great potential to take a step forward in the knowledge of this field, approaching well-known limitations in previous works that should be overcome.

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Section: Alternative Methodsmentioning

confidence: 99%

A Survey on Multi-robot Patrolling Algorithms

Portugal

Rocha

2011

IFIP Advances in Information and Communication Technology

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“…There is no communication among the agents. The approach is generalized to weighted graphs in Almeida et al (2004 studied adaptive agents that learn to patrol weighted graphs to minimize the time intervals between visits to the nodes, using Reinforcement Learning (RL) techniques. A Markov Decision Process (MDP) formalism was used to model the patrolling problem, and the challenge was to define a state and action space for each agent individually, and to develop proper models of instantaneous rewards which could lead to satisfactory long term performance.…”

Section: Background and Reviewmentioning

confidence: 99%

Multi-robot repeated area coverage

2013

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We address the problem of repeated coverage of a target area, of any polygonal shape, by a team of robots having a limited visual range. Three distributed Clusterbased algorithms, and a method called Cyclic Coverage are introduced for the problem. The goal is to evaluate the performance of the repeated coverage algorithms under the effects of the variables: Environment Representation, and the Robots' Visual Range. A comprehensive set of performance metrics are considered, including the distance the robots travel, the frequency of visiting points in the target area, and the degree of balance in workload distribution among the robots. The Cyclic Coverage approach, used as a benchmark to compare the algorithms, produces optimal or near-optimal solutions for the single robot case under some criteria. The results can be used as a framework for choosing an appropriate combination of repeated coverage algorithm, environment representation, and the robots' visual range based on the particular scenario and the metric to be optimized.

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“…Pioneer work on this field was presented in [3] and [4], where very simple approaches with reactive and cognitive agents were proposed as well as a first evaluation criterion based on idleness. Following these works, more advanced approaches based on graph theory became also popular like [5] and [6], which use graph partitioning to assign efficient patrol routes to different robots.…”

Section: Related Workmentioning

confidence: 99%

“…Five state-of-the art patrolling algorithms were implemented, namely: Conscientious Reactive (CR) [3], Heuristic Conscientious Reactive (HCR) [4], Heuristic Pathfinder Conscientious Cognitive (HPCC) [4], Cyclic Algorithm for Generic Graphs (CGG) [5] and Generalized MSP Algorithm (MSP) [5]. These algorithms were combined with six different teamsizes: 1, 2, 4, 6, 8 and 12; and three environments with different connectivity properties: low connectivity (A), medium connectivity (B) and high connectivity (C).…”

Section: Preliminariesmentioning

confidence: 99%

Measuring Variables Effect to Statistically Model the Multi-Robot Patrolling Problem by Means of ANOVA

Portugal

Rocha

2012

Technological Innovation for Value Creation

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Abstract. This paper focuses on analyzing extensive results generated from running diverse multi-robot patrolling algorithms with different configurations towards measuring the influence of the variables of the general problem. In order to do this, a statistical technique by the name of Analysis of Variance (ANOVA) is employed to compare the parameters and identify the ones which give raise to the total dispersion of the data set, at the same time accessing their contribution to the obtained results. It is shown that by applying such technique, it is possible to compute a data-related model for the Multi-Robot Patrolling Problem (MRPP).

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Recent Advances on Multi-agent Patrolling

Cited by 108 publications

References 5 publications

A Survey on Multi-robot Patrolling Algorithms

A Survey on Multi-robot Patrolling Algorithms

Multi-robot repeated area coverage

Measuring Variables Effect to Statistically Model the Multi-Robot Patrolling Problem by Means of ANOVA

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