A Survey on Multi-robot Patrolling Algorithms

Portugal, David; Rocha, Rui P.

doi:10.1007/978-3-642-19170-1_15

Cited by 82 publications

(48 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The performance of this approach is slightly better, but still deterministic. A survey of multi-agent patrolling strategies can be found in [16]. where strategies are evaluated based on robot perception, communication, coordination, and decision-making capabilities.…”

Section: Related Workmentioning

confidence: 99%

Decentralized and Nondeterministic Multi-Robot Area Patrolling in Adversarial Environments

Alam¹

2016

IJCA

View full text Add to dashboard Cite

Multi-robot patrolling is the problem of repeatedly visiting a group of regions of interest in an environment with a group of robots to prevent intrusion. Early works have proposed deterministic patrolling algorithms which could be learned by an adversary observing them over time. More recent works provide non-deterministic patrolling schemes which only work for perimeter patrolling and require coordination and synchronization. In this paper, we investigate the problem of finding robust and scalable strategies for multi-robot patrolling under an adversarial environment. So, we present algorithms to find different decentralized strategies for a patroller in the form of Markov chains which use convex optimization to minimize the average commute time for an environment, a subset of the environment, or a specific region of an environment when we use uniform distribution over all regions for both patroller and adversary. Additionally, we use these strategies in a game theoretical setup to form a payoff matrix to obtain an optimal mixed strategy for patroller. We also propose an algorithm to find a decentralized strategy for patroller in the form of Markov chain which converges very fast as it is the optimal response from patroller against the adversary when we use non-uniform distribution over all the regions for both patroller and adversary. Our results show the scalability and applicability of our approach in different types of environments. Despite the lack of synchronization and coordination among patrollers, our approach performs competitively compared to existing methods. General TermsMulti-robot patrolling

show abstract

Section: Related Workmentioning

confidence: 99%

Decentralized and Nondeterministic Multi-Robot Area Patrolling in Adversarial Environments

Alam¹

2016

IJCA

View full text Add to dashboard Cite

show abstract

“…A survey of the current state-of-the-art patrolling algorithms is provided in [14]. The representative approaches are evaluated in detail in [15] in terms of the average idleness of a patrolling graph and scalability to the number of agents metrics.…”

Section: Related Workmentioning

confidence: 99%

Model-predictive target defense by team of unmanned surface vehicles operating in uncertain environments

Raboin

Nau

Gupta

2013

2013 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

Abstract-In this paper, we present a heuristic planning approach for guarding a valuable asset by a team of autonomous unmanned surface vehicles (USVs) operating in a continuous state-action space. The team's objective is to maximize the amount of time it takes an intruder boat to reach the asset. The team must cooperatively deal with uncertainty about which boats are actual intruders, employ active blocking to slow down intruders' movement towards the asset, and intelligently distribute themselves around the target to optimize future guarding opportunities. Our planner incorporates a marketbased algorithm for allocating tasks to individual USVs by forward-simulating the mission and assigning estimated utilities to candidate task-allocation plans. The planner can be automatically adapted to a specific mission by optimizing the behaviors used to fulfil individual tasks. We present detailed simulation results that demonstrate the effectiveness of our approach.

show abstract

“…In contrast, many authors have proposed new algorithms for multi-robot patrolling based on other concepts, like task allocation [8], negotiation mechanisms [9], swarm intelligence [10], neural networks [11] or game theory [12]. For a more complete survey of multirobot patrolling strategies, the interested reader is referred to [13].…”

Section: Related Workmentioning

confidence: 99%

“…Every patrolling algorithm implicated in the results set, assume a topological environment, which is a common assumption in the MRPP [13], and the decisionmaking process is related to the computation of paths in the navigation graph so that robots effectively patrol a given environment, visiting all vertices of the graph. Since they are governed by different patrolling rules, it is necessary to define an evaluation metric to compare the performance of these algorithms.…”

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

Self Cite

View full text Add to dashboard Cite

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).

show abstract

A Survey on Multi-robot Patrolling Algorithms

Cited by 82 publications

References 11 publications

Decentralized and Nondeterministic Multi-Robot Area Patrolling in Adversarial Environments

Decentralized and Nondeterministic Multi-Robot Area Patrolling in Adversarial Environments

Model-predictive target defense by team of unmanned surface vehicles operating in uncertain environments

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

Contact Info

Product

Resources

About