A swarm approach for emission sources localization

Cui, X.; Hardin, C.T.; Ragade, Rammohan K.; Elmaghraby, Adel

doi:10.1109/ictai.2004.20

Cited by 47 publications

(34 citation statements)

References 11 publications

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“…These works can be classified as cooperative target tracking. Other related works were mainly devoted to developing decentralized tracking strategies of robot swarms or mobile sensor networks for multiple stationary targets [9][10][11]. Krishnanand et al addressed a problem for multiple odor source localization using mobile robot swarms [9].…”

Section: Introductionmentioning

confidence: 99%

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Tracking multiple moving targets with swarms of mobile robots

Lee

Chong

Christensen

2010

Intel Serv Robotics

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This paper presents a distributed approach to enable mobile robot swarms to track multiple targets moving unpredictably. The proposed approach consists of two constituent algorithms: local interaction and target tracking. When the robots are faster than the targets, Lyapunov theory can be applied to show that the robots converge asymptotically to each vertex of the desired equilateral triangular configurations while tracking the targets. Toward practical implementation of the algorithms, it is important to realize the observation capability of individual robots in an inexpensive and efficient way. A new proximity sensor that we call dual rotating infrared (DRIr) sensor is developed to meet these requirements. Both our simulation and experimental results employing the proposed algorithms and DRIr sensors confirm that the proposed distributed multi-target tracking method for a swarm of robots is effective and easy to implement.

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Section: Introductionmentioning

confidence: 99%

“…It may offer many advantages over a single robot in terms of efficiency, faulttolerance, adaptability, and so on [1,2]. Exploiting such features that swarms of robots can exhibit, new applications have emerged, such as order localization or plume tracing [3][4][5][6], and have expanded to support multiple target tracking [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Tracking multiple moving targets with swarms of mobile robots

Lee

Chong

Christensen

2010

Intel Serv Robotics

View full text Add to dashboard Cite

show abstract

“…Concentration gradient climbing (chemotaxis (Russell et al, 2003;Grasso et al, 1997)) and up-wind directed search (anemotaxis Marjovi and Marques, 2011;Lochmatter et al, 2010)) are the most common approaches to track odor plumes by mobile robots. Several other methods have been proposed for plume tracking using swarm robotic concepts, namely, biasing expansion swarm approach (BESA) (Cui et al, 2004), biased random walk (BRW) (Marques et al, 2002), particle swarm optimization (PSO) (Li et al, 2008;, glowworm swarm optimization (GSO) (Krishnanand and Ghose, 2008), gradient climbing techniques (Marjovi et al, 2010b), swarm spiral surge (Kazadi, 2003), and physics-based swarming approach (Zarzhitsky et al, 2005). Most of these studies (e.g.…”

Section: Introductionmentioning

confidence: 99%

Optimal spatial formation of swarm robotic gas sensors in odor plume finding

Marjovi

Marques

2013

Auton Robot

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Finding the best spatial formation of stationary gas sensors in detection of odor clues is the first step of searching for olfactory targets in a given space using a swarm of robots. Considering no movement for a network of gas sensors, this paper formulates the problem of odor plume detection and analytically finds the optimal spatial configuration of the sensors for plume detection, given a set of assumptions. This solution was analyzed and verified by simulations and finally experimentally validated in a reduced scale realistic environment using a set of Roomba-based mobile robots.

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“…Bio-inspired [16], [17], concentration gradient climbing (chemotaxis) and up-wind directed search (anemotaxis [19], [24], [25]) are the most common approaches to track odor plumes by mobile robots. Several other methods have been proposed for plume tracking using swarm robotic concepts, namely, biasing expansion swarm approaches (BESA) [26], biased random walk (BRW), evolutionary strategies [27], particle swarm optimization (PSO) [28]- [30], glowworm swarm optimization (GSO) [31], gradient climbing techniques, swarm spiral surge [32], physics-based swarming approach [33], and attraction/repulsion forces [15]. Most of these studies (e.g.…”

Section: Introductionmentioning

confidence: 99%

Optimal Swarm Formation for Odor Plume Finding

Marjovi

Marques

2014

IEEE Trans. Cybern.

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Abstract-This paper presents an analytical approach to the problem of odor plume finding by a network of swarm robotic gas sensors and finds an optimal configuration for them, given a set of assumptions. Considering cross-wind movement for the swarm, we found that the best spatial formation of robots in finding odor plumes is diagonal line configuration with equal distance between each pair of neighboring robots. We show that the distance between neighboring pairs in the line topology depends mainly on the wind speed and the environmental conditions, whereas, the number of robots and the swarm's crosswind movement distance do not show significant impact on optimal configurations. These solutions were analyzed and verified by simulations and experimentally validated in a reduced scale realistic environment using a set of mobile robots.

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A swarm approach for emission sources localization

Cited by 47 publications

References 11 publications

Tracking multiple moving targets with swarms of mobile robots

Tracking multiple moving targets with swarms of mobile robots

Optimal spatial formation of swarm robotic gas sensors in odor plume finding

Optimal Swarm Formation for Odor Plume Finding

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