Multirobot cooperation for surveillance of multiple moving targets - a new behavioral approach

Kolling, Andreas; Carpin, Stefano

doi:10.1109/robot.2006.1641890

Cited by 37 publications

(54 citation statements)

References 6 publications

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“…The performance of the proposed approach is evaluated by comparing it with four different approaches with different observation policies: (1)Local approach [123], (2)A- CMOMMT approach [123], (3)B-CMOMMT approach [157], and (4)Modified B-CMOMMT [49].…”

Section: Cooperative Multi-target Tracking Simulationsmentioning

confidence: 99%

“…The B-CMOMMT approach is essentially proposed to overcome some problematic situations that may arise in the A-CMOMMT approach. More detailed discussions of these situations can be found in [49,157]. The most important difference between A-CMOMMT and B-CMOMMT is that the force vectors coming from nearby trackers are weighted as well.…”

Section: Cooperative Multi-target Tracking Simulationsmentioning

confidence: 99%

See 1 more Smart Citation

Market-Based Framework for Mobile Surveillance Systems

Elmogy

Khamis²,

Karray

2012

Autonomous and Intelligent Systems

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Section: Cooperative Multi-target Tracking Simulationsmentioning

confidence: 99%

Section: Cooperative Multi-target Tracking Simulationsmentioning

confidence: 99%

Market-Based Framework for Mobile Surveillance Systems

Elmogy

Khamis²,

Karray

2012

Autonomous and Intelligent Systems

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“…Research challenges include developing tools that can predict the global behavior given a set of local control rules, as well as the inverse problem, in which we want to derive the local control rules, given a desired global behavior. This paradigm is relevant for many spatially distributed applications, including flocking, schooling, and formations (e.g., [92], [70], [5], [81], [105], [73], [75], [76], [8]); foraging, coverage, and search (e.g., [87], [31], [120], [34], [4], [104], [94], [106], [16], [69]); target tracking and observation (e.g., [7], [123], [65], [62], [57], [51], [111]); sorting and clumping [8], and so forth. While earlier approaches to these applications were based on human-generated local control rules that were demonstrated to work in practice, more recent work is based on control theoretic principles, with a focus on proving stability and convergence properties in multi-robot team behaviors.…”

Section: A Bioinspired Emergent Swarms Paradigmmentioning

confidence: 99%

Distributed intelligence: overview of the field and its application in multi-robot systems

Parker¹

2008

jopha

148

107

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Abstract-This article overviews the concepts of distributed intelligence, outlining the motivations for studying this field of research. First, common systems of distributed intelligence are classified based upon the types of interactions exhibited, since the type of interaction has relevance to the solution paradigm to be used. We outline three common paradigms for distributed intelligence -the bioinspired paradigm, the organizational and social paradigm, and the knowledge-based, ontological paradigm -and give examples of how these paradigms can be used in multi-robot systems. We then look at a common problem in multirobot systems -that of task allocation -and show how the solution approach to this problem is very different depending upon the paradigm chosen for abstracting the problem. Our conclusion is that the paradigms are not interchangeable, but rather the selection of the appropriate paradigm is dependent upon the specific constraints and requirements of the application of interest. Further work is needed to provide guidance to the system designer on selecting the proper abstraction, or paradigm, for a given problem.

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“…Kolling and Carpin [17,18] presented another version of the Parker work called B-COMMT to overcome some of the difficulties exist in Parker work. One of these situations was when one robot followed two targets moving in opposite directions, eventually losing both.…”

Section: Related Workmentioning

confidence: 99%