Swarms for chemical plume tracing

Zarzhitsky, Dimitri; Spears, Diana F.; Spears, William M.

doi:10.1109/sis.2005.1501629

Cited by 51 publications

(29 citation statements)

References 22 publications

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“…Other recent developments for this purpose can be found in Bertozzi et al (2005), Hsieh and Kumar (2006), Zarzhitsky et al (2005), Andersson and Park (2005), Clark and Fierro (2005) and Susca et al (2006). In those papers coherent patterns are established using methods that are different from ours.…”

Section: Introductionmentioning

confidence: 99%

Control of coordinated patterns for ocean sampling

Zhang

Fratantoni

Paley

et al. 2007

International Journal of Control

174

123

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A class of underwater vehicles are modelled as Newtonian particles for navigation and control. We show a general method that controls cooperative Newtonian particles to generate patterns on closed smooth curves. These patterns are chosen for good sampling performance using mobile sensor networks. We measure the spacing between neighbouring particles by the relative curve phase along the curve. The distance between a particle and the desired curve is measured using an orbit function. The orbit value and the relative curve phase are then used as feedback to control motion of each particle. From an arbitrary initial configuration, the particles converge asymptotically to form an invariant pattern on the desired curves. We describe application of this method to control underwater gliders in a field experiment in Buzzards Bay, MA in March 2006.

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Section: Introductionmentioning

confidence: 99%

Control of coordinated patterns for ocean sampling

Zhang

Fratantoni

Paley

et al. 2007

International Journal of Control

174

123

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“…Leveraging multiple robots for distributed search has its own peculiarities, many of which have been explored during the last decade. Multirobot search algorithms proposed in the literature include biasing expansion swarm approaches [11], biased random walk [12], particle swarm optimization [13], gradient climbing techniques [14], infotaxis [15], probabilistic reasoning [16], search through exploration [17], physics-based swarming [18], attraction-repulsion swarming [19], and formation-based algorithms [20], [21].…”

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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“…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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Swarms for chemical plume tracing

Cited by 51 publications

References 22 publications

Control of coordinated patterns for ocean sampling

Control of coordinated patterns for ocean sampling

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

Optimal Swarm Formation for Odor Plume Finding

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