A Mobile Robots PSO-based for Odor Source Localization in Dynamic Advection-Diffusion Environment

Jatmiko, Wisnu; Sekiyama, Kosuke; Fukuda, Toshio

doi:10.1109/iros.2006.282092

Cited by 22 publications

(10 citation statements)

References 14 publications

(20 reference statements)

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“…PSO has widely been employed in miscellaneous field, to cite an instance swarm robot for odour source localization purpose [14,15]. PSO algorithm consist several consecutive steps.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Particle Swarm Optimization (Pso) for Training Optimization on Convolutional Neural Network (Cnn)

Syulistyo

Purnomo

Rachmadi

et al. 2016

Jurnal Ilmu Komputer dan Informasi

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Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO) in Convolutional Neural Networks (CNNs), which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN. The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.

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“…PSO has widely been employed in miscellaneous field, to cite an instance swarm robot for odour source localization purpose [14,15]. PSO algorithm consist several consecutive steps.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Particle Swarm Optimization (Pso) for Training Optimization on Convolutional Neural Network (Cnn)

Syulistyo

Purnomo

Rachmadi

et al. 2016

Jurnal Ilmu Komputer dan Informasi

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“…Step 1 Initialize t = 0, V/O), X/OJ Calculate P/O), PlO) Step 2 Repeat for each particle-i ( Update �(t) Using (1); Update X/t) Using (2); Update P/t) Using (3); Update Plt) Using (4) Detect and response have been studied previously by researchers [2][3][4][5][6][7].…”

Section: Standard Particle Swarm Optimizationmentioning

confidence: 99%

Modified Particle Swarm for multimodal functions in dynamic environment using iteration proportional change for inertia weight and weight of social components

Widiyanto

Wibowo

Rachmadi

et al. 2012

2012 International Symposium on Micro-NanoMechatronics and Human Science (MHS)

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Abtract:This paper proposes a modified Particle Swarm Optimization (PSO) for multimodal function in a dynamic environment, with approaches, that uses inertia weight and change of the social component that is proportional to the number of iterations of the particle swarm.In multimodal case, PSO is expected to be able to find global and local optima, and to avoid trapped particles in the local optima before finding global optima. This study proposes the use of a parallel niche (sub swarm), with each niche having its own best value optima, and does not share these values. In dynamic environment case, PSO should find the optima value in every change of fitness function. Previous research has introduced detect and response method as a PSO solution for dynamic environments. This study proposes placing randomly sentry particles to detect the occurrence of changed fitness function for phase detection.Responding phase is done by searching optima value using improving velocity formula in social only mode.The weight of social component is accelerated to converge towards the optima value and decelerated to diverge by a sinusoidal function with period 0 and phi, and the inertia speed will be accelerated to the opposite direction. Finally, the algorithm has been tested using a benchmarking formula and it has shown a better result.

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“…Instead, it is far more interesting to use a mobile sampling scheme that would collect samples at few judiciously selected locations, in a way that would enable it to gain enough information about the field to be able to infer, with significant accuracy, the value of the parameter of interest at the unsampled locations. A multitude of research groups have published results on sampling using mobile robots for chemical plume source localization [1,2], soil-moisture mapping for crop monitoring [3], ocean sampling [4,5], forest-fire mapping [6], etc.…”

Section: Introductionmentioning

confidence: 99%

A distributed multi-robot adaptive sampling scheme for the estimation of the spatial distribution in widespread fields

Mysorewala

Cheded

Popa

2012

J Wireless Com Network

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Monitoring widespread environmental fields is undoubtedly a practically important area of research with many complex and challenging tasks. It involves the building of models of the fields or natural phenomena to be monitored, the estimation of the spatio-temporal distribution of a variety of environmental parameters of interest, such as moisture or salinity in a crop field, or the spatial distribution of vital natural resources such as oil and gas, etc. Sampling, a key operation of the monitoring process, is a broad methodology for gathering statistical information about the phenomenon, or environmental variable, being monitored. To efficiently monitor widespread fields and estimate the spatio-temporal distribution of some particular environmental variable, calls for the use of a sampling strategy can fuse information from different scales of sensors. Such an attractive strategy is well catered for by both the capabilities and distributed nature of wireless sensor networks and the mobility of robots performing the sampling (sensing) tasks. This sampling strategy could even be rendered "adaptive" in that the decision of "where to sample next" evolves temporally with past measurements and is optimally computed. In this article, we examine various single-robot and multi-robot adaptive sampling schemes based on different extended Kalman filter filtering structures such as centralized and decentralized filters as well as our own novel decentralized and distributed filters. Our investigation shows that, whereas the first two filters suffer from a heavy computational or communication load, our proposed method, through its key feature of distributing the filtering task amongst the robots used, manages to reduce both loads and the total reconstruction time. It also enjoys the added attractive feature of scalability that allows the structure of the proposed monitoring scheme to grow with the complexity of the field under study. Our results are corroborated by our simulation work and offer ample encouragement for a further theoretical investigation of some properties of the proposed scheme and its implementation on a physical system. Both of these activities are currently underway.

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A Mobile Robots PSO-based for Odor Source Localization in Dynamic Advection-Diffusion Environment

Cited by 22 publications

References 14 publications

Particle Swarm Optimization (Pso) for Training Optimization on Convolutional Neural Network (Cnn)

Particle Swarm Optimization (Pso) for Training Optimization on Convolutional Neural Network (Cnn)

Modified Particle Swarm for multimodal functions in dynamic environment using iteration proportional change for inertia weight and weight of social components

A distributed multi-robot adaptive sampling scheme for the estimation of the spatial distribution in widespread fields

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