Dynamic plume tracking using mobile sensors

Sahyoun, Samir; Djouadi, Seddik M.; Qi, Hairong

doi:10.1109/acc.2010.5531529

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…Extremum-seeking is applied in a collaborative manner in Ghods and Krstić (2010), in a 1-dimensional framework. Two distributed stochastic source localizations are in Sahyoun, Djouadi, and Qi (2010), where a group of chemical sensors takes measures of a plume concentration values to estimate the source position via a stochastic approximation technique, and in Rabbat and Nowak (2004), in which the authors use the sensor measurements to estimate the model parameters of the concentration plume. A collaborative control law to steer a fleet of AUVs (autonomous underwater vehicles) to the source of a signal distribution using only direct signal measurements by a circular formation of agents is presented in Briñón Arranz, Seuret, and Canudas de Wit (2011) and Moore and Canudas de Wit (2010); these ideas are formalized and extended in Canudas de Wit, Garin, Fabbiano, Rouchon, and Rousseau (2012).…”

Section: Overview Of Source-seekingmentioning

confidence: 99%

Source localization by gradient estimation based on Poisson integral

2014

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We consider the problem of localizing the source of a diffusion process. The source is supposed to be isotropic, and several sensors, equipped on a vehicle moving without position information, provide pointwise measures of the quantity being emitted. The solution we propose is based on computing the gradient-and higher-order derivatives such as the Hessian-from Poisson integrals: in opposition to other solutions previously proposed, this computation does neither require specific knowledge of the solution of the diffusion process, nor the use of probing signals, but only exploits properties of the PDE describing the diffusion process. The theoretical results are illustrated by simulations.

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Section: Overview Of Source-seekingmentioning

confidence: 99%

Source localization by gradient estimation based on Poisson integral

2014

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“…A leastsquares approximation is applied in order to steer the group of agents to the source location. Other works are based on distributed estimation of the concentration plume [24], [25]. In this case, the function signal is estimated or approximated and the source localization becomes a distributed optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Distributed Source Seeking via a Circular Formation of Agents Under Communication Constraints

Briñón-Arranz

Schenato

Seuret

2016

IEEE Trans. Control Netw. Syst.

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Abstract-This paper addresses the source seeking problem in which a group of autonomous vehicles must locate and follow the source of some signal based on measurements of the signal strength at different positions. Based on the observation that the gradient of the signal strength can be approximated by a circular formation of agents via a simple weighted average of the signal measured by each agent, we propose a combination of a cooperative control law to stabilize the agents to a circular formation and a distributed consensus-based source seeking algorithm, which is guaranteed to steer the circular formation towards the vicinity of the source location. In particular, the proposed algorithm is provided with two tunable parameters that allow for a tradeoff between speed of convergence, noise filtering and formation stability. The benefit of using consensus-based algorithms resides in a more realist discrete time control of the agents and in asynchronous communication resilient to delays, which is particularly relevant for underwater applications. The analytic results are complemented with numerical simulations.

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“…A least-squares approximation is applied in order to steer the group of agents to the source location. Other works are based on distributed estimation of the concentration plume [17], [16]. In this case, the function signal is estimated or approximated and the source localization becomes a distributed optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Consensus-based source-seeking with a circular formation of agents

Briñón-Arranz

Schenato

2013

2013 European Control Conference (ECC)

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This paper deals with the source-seeking problem in which a group of autonomous vehicles must locate and follow the source of some signal based on measurements of the signal strength at different positions. First, we show that the gradient of the signal strength can be approximated by a circular formation of agents via a simple weighted average of the signal strength measured by the agents. Then, we propose a distributed source-seeking algorithm based on a consensus method which is guaranteed to steer the circular formation towards the source location using the estimated gradient direction. The proposed algorithm is provided with two tunable parameters that allow for a tradeoff between speed of convergence, noise filtering and formation stability. The benefit of using consensus-based algorithms resides in a more realist discrete time control of the agents and in asynchronous communication resilient to delays which is particularly relevant for underwater applications. The analytical results are finally complemented with numerical simulations.

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Dynamic plume tracking using mobile sensors

Cited by 11 publications

References 16 publications

Source localization by gradient estimation based on Poisson integral

Source localization by gradient estimation based on Poisson integral

Distributed Source Seeking via a Circular Formation of Agents Under Communication Constraints

Consensus-based source-seeking with a circular formation of agents

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