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

Briñón-Arranz, Lara; Schenato, Luca

doi:10.23919/ecc.2013.6669782

Cited by 20 publications

(18 citation statements)

References 22 publications

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“…This helps to drive the formation of agents towards the source [10 • ], [12,23,35,42,50,51,52,57,58,59,60]. Since multi-agent systems provide flexibility, maneuverability to explore regions, and more information, we envision that they hold a great potential and will likely dominate future research directions in the source localization problem.…”

Section: Discussionmentioning

confidence: 99%

Environmental monitoring using autonomous vehicles: a survey of recent searching techniques

Bayat

Crasta

Crespi

et al. 2017

Current Opinion in Biotechnology

145

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Autonomous vehicles are becoming an essential tool in a wide range of environmental applications that include ambient data acquisition, remote sensing, and mapping of the spatial extent of pollutant spills. Among these applications, pollution source localization has drawn increasing interest due to its scientific and commercial interest and the emergence of a new breed of robotic vehicles capable of performing demanding tasks in harsh environments without human supervision. In this task, the aim is to find the location of a region that is the source of a given substance of interest (e.g. a chemical pollutant at sea or a gas leakage in air) using a group of cooperative autonomous vehicles. Motivated by fast paced advances in this challenging area, this paper surveys recent advances in searching techniques that are at the core of environmental monitoring strategies using autonomous vehicles.

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Section: Discussionmentioning

confidence: 99%

Environmental monitoring using autonomous vehicles: a survey of recent searching techniques

Bayat

Crasta

Crespi

et al. 2017

Current Opinion in Biotechnology

145

View full text Add to dashboard Cite

show abstract

“…In this situation, the fleet of vehicles can be seen as a mobile sensor network. We exploit preliminary results from [4], [5] in which a group of vehicles, uniformly distributed in a circular formation, is able to estimate the gradient of the measured signal. No prior knowledge of the environment or convexity of the signal field is required.…”

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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“…The advantages of robot swarm and sensor network attract many researchers to study them and apply them to the source seeking problem. In [17], [18] and [19], a team of agents implement a consensus algorithm to maintain a particular formation to track the gradient of the source. This method assumes the formation of the swarm being maintained perfectly which is too ideal to achieve.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-Based Source Seeking

Zou

Kalivarapu

Winer

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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The task of locating a source based on the measurements of the signal emitted/emanating from it is called the source-seeking problem. In the past few years, there has been a lot of interest in deploying autonomous platforms for source-seeking. Some of the challenging issues with implementing autonomous source-seeking are the lack of a priori knowledge about the distribution of the emitted signal and presence of noise in both the environment and on-board sensor measurements. This paper proposes a planner for a swarm of robots engaged in seeking an electromagnetic source. The navigation strategy for the planner is based on Particle Swarm Optimization (PSO) which is a population-based stochastic optimization technique. An equivalence is established between particles generated in the traditional PSO technique, and the mobile agents in the swarm. Since the positions of the robots are updated using the PSO algorithm, modifications are required to implement the PSO algorithm on real robots to incorporate collision avoidance strategies. The modifications necessary to implement PSO on mobile robots, and strategies to adapt to real environments are presented in this paper. Our results are also validated on an experimental testbed. Note to Practitioners-This paper is inspired by the source seeking problem in which the signal emitted from the source is assumed to be very noisy, and the spatial distribution is assumed to be non-smooth. We focus our work specifically on electromagnetic sources. However, the strategies proposed in this paper are also applicable to other kinds of sources, for example, nuclear, radiological, chemical or biological. We develop a planner for a swarm of mobile agents that try to locate an unknown electromagnetic source. The mobile agents know their own positions and can measure the signal strength at their current location. They can share information among themselves, and plan for the next step. We propose a complete solution to ensure the effectiveness of PSO in complex environments where collisions may occur. We incorporate static and dynamic obstacle avoidance strategies in PSO to make it fully applicable to real-world scenario. We validate the proposed technique on an experimental testbed. As a part of our future work, we will extend the technique to locate multiple sources of different kinds. Abstract-Signal source seeking using autonomous vehicles is a complex problem. The complexity increases manifold when signal intensities captured by physical sensors onboard are noisy and unreliable. Added to the fact that signal strength decays with distance, noisy environments make it extremely difficult to describe and model a decay function. This paper addresses our work with seeking maximum signal strength in a continuous electromagnetic signal source with mobile robots, using Particle Swarm Optimization (PSO). A one to one correspondence with swarm members in a PSO and physical mobile robots is established and the positions of the robots are iteratively updated as the PSO algorithm proceeds...

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

Cited by 20 publications

References 22 publications

Environmental monitoring using autonomous vehicles: a survey of recent searching techniques

Environmental monitoring using autonomous vehicles: a survey of recent searching techniques

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

Particle Swarm Optimization-Based Source Seeking

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