Efficient Multi-robot Search for a Moving Target

Hollinger, Geoffrey A.; Singh, Sanjiv; Djugash, Joseph; Kehagias, Athanasios

doi:10.1177/0278364908099853

Cited by 146 publications

(152 citation statements)

References 26 publications

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“…Finally, we refer the reader interested in more practical aspects of connected graph searching (including distributed computations) to works on algorithms and applications in the field of robotics [29,30,33,34,41].…”

Section: Related Workmentioning

confidence: 99%

Distributed graph searching with a sense of direction

2014

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In this work we consider the edge searching problem for vertex-weighted graphs with arbitrarily fast and invisible fugitive. The weight function ω provides for each vertex v the minimum number of searchers required to guard v, i.e., the fugitive may not pass through v without being detected only if at least ω(v) searchers are present at v. This problem is a generalization of the classical edge searching problem, in which one has ω ≡ 1. We assume that with a graph G to be searched, there is associated a partition (V 1 , . . . , V t ) of its vertex set such that edges are allowed only within each V i and between two consecutive V i 's. We provide an algorithm for distributed monotone connected edge searching of such graphs, where the searchers are initially placed on an arbitrary vertex of G and have no a priori knowledge on G, but they have a sense of direction that lets them recognize whether an edge incident to already explored vertex in V i leads to a vertex in one of V i−1 , V i or V i+1 . Starting from any vertex the algorithm uses at most 3·max i=1,...,t ω(V i )+1 searchers, where ω(V i ) = v∈V i ω(v). We also prove that this algorithm is best possible up to a small additive constant, that is, each distributed searching algorithm in worst case must use 3 · max i=1,...,t ω(V i ) − 1 searchers for some graphs.

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Section: Related Workmentioning

confidence: 99%

Distributed graph searching with a sense of direction

2014

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“…The Markovian assumption does not allow any target model that requires a history. Hollinger et al (2009) also examined a receding horizon approximation algorithm to the search POMDP that achieves performance guarantees by sequentially allocating the search effort, requiring only linear computation in the team size. As in general POMDP methods, they also assume a Markovian motion model, and the computation of their method increases exponentially with the horizon length.…”

Section: Probabilistic Search Methods In Roboticsmentioning

confidence: 99%

Search and pursuit-evasion in mobile robotics

2011

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This paper surveys recent results in pursuitevasion and autonomous search relevant to applications in mobile robotics. We provide a taxonomy of search problems that highlights the differences resulting from varying assumptions on the searchers, targets, and the environment. We then list a number of fundamental results in the areas of pursuit-evasion and probabilistic search, and we discuss field implementations on mobile robotic systems. In addition, we highlight current open problems in the area and explore avenues for future work.

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“…This has much in common with search and rescue, and with humanitarian demining-our target areas of application. Techniques have been developed to ensure that the entire boundary of a space is visited [99], that search finds a specific target [6,7,41,42,80,84], that a mobile target is kept under constant observation [75,68], and that a human-robot team can exchange search roles flexibly and appropriately [43].…”

Section: Related Workmentioning

confidence: 99%

Designing the HRTeam Framework: Lessons Learned from a Rough-and-Ready Human/Multi-Robot Team

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Muñoz

et al. 2012

Advanced Agent Technology

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Abstract. In this workshop paper, we share the design and on-going implementation of our HRTeam framework, which is constructed to support multiple robots working with a human operator in a dynamic environment. The team is comprised of one human plus a heterogeneous set of inexpensive, limited-function robots. Although each individual robot has restricted mobility and sensing capabilities, together the team members constitute a multi-function, multi-robot facility. We describe low-level system architecture details and explain how we have integrated a popular robotic control and simulation environment into our framework to support application of multi-agent techniques in a hardware-based environment. We highlight lessons learned regarding the integration of multiple varying robot platforms into our system, from both hardware and software perspectives. Our aim is to generate discussion amongst multirobot researchers concerning issues that are of particular interest and present particular difficulties to the multi-robot systems community.

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Efficient Multi-robot Search for a Moving Target

Cited by 146 publications

References 26 publications

Distributed graph searching with a sense of direction

Distributed graph searching with a sense of direction

Search and pursuit-evasion in mobile robotics

Designing the HRTeam Framework: Lessons Learned from a Rough-and-Ready Human/Multi-Robot Team

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