A PSO-based Mobile Sensor Network for Odor Source Localization in Dynamic Environment: Theory, Simulation and Measurement

Jatmiko, Wisnu; Sekiyama, Kosuke; Fukuda, T.

doi:10.1109/cec.2006.1688423

Cited by 47 publications

(35 citation statements)

References 12 publications

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“…Moreover, another extension is called atomic PSO where half of the particles have the extra charge acceleration term and the other half are neutral. After adding a repulsion function to make balancing diversity, Jatmiko [12] applied it to an odor source localization task.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…The gbest and second gbest value are monitored to check if they have no changes for 20 iterations in [22]. Similar to this, monitoring the gbest and 20 iteration intervals are adopted in [12].…”

Section: Environment Change Detectionmentioning

confidence: 99%

“…For example, particles' personal best vectors are replaced with their current position vector on a regular frequency based on the iteration to forget their experience [5,17,18,23,25]. In [12], one strategy is designed to restart the gbest fitness value at the initial value (gbest=0) in order to make all particles jump out to follow the changes. It implies loss of information gathered during the search so far.…”

Section: Proactive Strategymentioning

confidence: 99%

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Simpson¹

2008

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Environment Change Detectionmentioning

confidence: 99%

Section: Proactive Strategymentioning

confidence: 99%

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Simpson¹

2008

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“…The first versions of PSO were proposed in [1,2,8] on a multi-robot search system to find a target in the environment, and studies have demonstrated that the PSO algorithm has an acceptable performance in the searching task. In several instances, adaptations of PSO have been used for multi-robot odour searches [9,10]. Adapted versions of PSO on distributed mobile robots have been used to search the environment based on only local information [2,11].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid of Modified PSO and Local Search on a Multi-Robot Search System

Rastgoo

Nakisa

Nazri

2015

International Journal of Advanced Robotic Systems

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Particle swarm optimization (PSO), a new populationbased algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obstacles. One of these defects is premature convergence. This means that one of the properties of basic PSO is that when particles are spread in a search space, as time increases they tend to converge in a small area. This shortcoming is also evident on a multi-robot search system, particularly when there are big static obstacles in the search space that prevent the robots from finding the target easily; therefore, as time increases, based on this property they converge to a small area that may not contain the target and become entrapped in that area. Another shortcoming is that basic PSO cannot guarantee the global convergence of the algorithm. In other words, initially particles explore different areas, but in some cases they are not good at exploiting promising areas, which will increase the search time.This study proposes a method based on the particle swarm optimization (PSO) technique on a multi-robot system to find a target in a search space containing big static obstacles. This method is not only able to overcome the premature convergence problem but also establishes an efficient balance between exploration and exploitation and guarantees global convergence, reducing the search time by combining with a local search method, such as A-star.To validate the effectiveness and usefulness of algorithms, a simulation environment has been developed for conducting simulation-based experiments in different scenarios and for reporting experimental results. These experimental results have demonstrated that the proposed method is able to overcome the premature convergence problem and guarantee global convergence.

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“…Distributed unsupervised robotic learning was accomplished in a robotic group by assigning each robot a unique PSO particle that represented the robot controller [19]. Adaptations of PSO have been used for multi-robot odor search in several instances [9], [15]. Particle Swarm Optimization was also applied recursively to a multi-robot search task, where the parameters of the PSO-inspired search were optimized by an external PSO algorithm [6].…”

Section: Introductionmentioning

confidence: 99%

Inspiring and Modeling Multi-Robot Search with Particle Swarm Optimization

Pugh

Martinoli

2007

2007 IEEE Swarm Intelligence Symposium

196

132

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Abstract-Within the field of multi-robot systems, multi-robot search is one area which is currently receiving a lot of research attention. One major challenge within this area is to design effective algorithms that allow a team of robots to work together to find their targets. Recently, techniques have been adopted for multi-robot search from the Particle Swarm Optimization algorithm, which uses a virtual multi-agent search to find optima in a multi-dimensional function space. We present here a multi-search algorithm inspired by Particle Swarm Optimization. Additionally, we exploit this inspiration by modifying the Particle Swarm Optimization algorithm to mimic the multi-robot search process, thereby allowing us to model at an abstracted level the effects of changing aspects and parameters of the system such as number of robots and communication range.

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A PSO-based Mobile Sensor Network for Odor Source Localization in Dynamic Environment: Theory, Simulation and Measurement

Cited by 47 publications

References 12 publications

RhoGTPase Involvement in Breast Cancer Migration and Invasion

RhoGTPase Involvement in Breast Cancer Migration and Invasion

A Hybrid of Modified PSO and Local Search on a Multi-Robot Search System

Inspiring and Modeling Multi-Robot Search with Particle Swarm Optimization

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