Multi-agent control algorithms for chemical cloud detection and mapping using unmanned air vehicles

Kovacina, Michael A.; Palmer, William D.; Yang, Guang; Vaidyanathan, Ravi

doi:10.1109/irds.2002.1041691

Cited by 65 publications

(34 citation statements)

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“…Several strategies have been devised to search for areas of interest in an environment with applications in the detection of chemical plumes (Kovacina et al, 2002;Lawrence et al, 2004;Oyekan and Huosheng, 2009;Zarzhitsky and Spears, 2005), wild fires (Gancet et al, 2005;Merino et al, 2006), victims (Kuiper and NadjmTehrani, 2006;Murphy et al, 2008), objects (Stirling et al, 2010) and other targets of interest (Altshuler et al, 2008;Cole et al, 2006;Ruini and Cangelosi, 2009;Sauter et al, 2005;Yang et al, 2005).…”

Section: Explorationmentioning

confidence: 99%

“…The straight forward way to coordinate an exploration task using multiple flying robots is to divide the environment into regions that will each be assigned to a separate robot as proposed by Kovacina et al (2002). Each robot can be assigned to one area or a set of waypoints by the operator.…”

Section: Explorationmentioning

confidence: 99%

“…Furthermore, multiple robots can share computing, sensing and communication payloads thus leading to lighter robots that could be safer than a larger system, easier to transport and even disposable in some cases (Holland et al, 2005). Deploying a fleet of unmanned aerial vehicles instead of a single aircraft allows rapid coverage of a relatively larger area or volume (Alidaee et al, 2009;Beard et al, 2006;Bertuccelli et al, 2004;Gancet et al, 2005;Kovacina et al, 2002;Kuiper and Nadjm-Tehrani, 2006;Lawrence et al, 2004;Merino et al, 2006;Parunak et al, 2005;Richards et al, 2002;Sauter et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Underwater Robot Swarms: Challenges and Opportunities

2013

Handbook of Collective Robotics

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Section: Explorationmentioning

confidence: 99%

Section: Explorationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Underwater Robot Swarms: Challenges and Opportunities

2013

Handbook of Collective Robotics

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“…Kuroki et al [22] presented an expert system for contaminant mapping based on a genetic algorithm for simulated Unmanned Aerial Vehicles (UAVs) and considering the Gaussian plume/puff model for odor distribution. In a simple simulation environment, Kovacina et al [23] presented a decentralized swarming algorithm for mapping a chemical cloud. This method considers the restrictions on sensors, computation, and flight envelope that affect aerial robots.…”

Section: Introductionmentioning

confidence: 99%

Towards 3-D distributed odor source localization: An extended graph-based formation control algorithm for plume tracking

Soares¹,

Marjovi²,

Giezendanner³

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-The large number of potential applications for robotic odor source localization has motivated the development of a variety of plume tracking algorithms, the majority of which work in restricted two-dimensional scenarios. In this paper, we introduce a distributed algorithm for 3-D plume tracking using a system of ground and aerial robots in formation. We propose an algorithm that takes advantage of spatially distributed measurements to track the plume in 3-D and lead the robots to the source by integrating three behaviors -upwind movement, plume centering, and Laplacian feedback formation control. We evaluate this strategy in simulation and with real robots in a wind tunnel. For a source close to the ground, results show that a team of robots running our algorithm reaches the source with low lateral error while also tracing the horizontal and vertical plume shape.

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“…Future robotic sensor networks are expected to provide ubiquitous perception for a wide variety of applications. These applications include in-situ volumetric sensing [1], multi-target surveillance [2], chemical plume detection and tracking [3], autonomous underwater networks [4], [5], and networked communication relaying [6].…”

Section: Introductionmentioning

confidence: 99%

Information-theoretic integration of sensing and communication for active robot networks

Frew¹

2007

The Proceedings of First International Conference on Robot Communication and Coordination

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Abstract-This paper presents an information-theoretic approach to sensor placement that incorporates communication capacity into an optimal formulation. A new formulation is presented that maximizes the information rate achievable by a set of sensors communicating wirelessly to a single collection node. Shannon capacity and the standard radio propagation model are used to model the throughput achievable by a sensor configuration. Likewise, the d-optimality criterion from the active sensing literature is used to model information gain provided by range and bearing sensors. The combiniation of informationtheoretic measures leads to a metric equivalent to the expected information rate achievable by the system. Sensor positions are selected that optimize this measure.

show abstract

Multi-agent control algorithms for chemical cloud detection and mapping using unmanned air vehicles

Cited by 65 publications

References 1 publication

Underwater Robot Swarms: Challenges and Opportunities

Underwater Robot Swarms: Challenges and Opportunities

Towards 3-D distributed odor source localization: An extended graph-based formation control algorithm for plume tracking

Information-theoretic integration of sensing and communication for active robot networks

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