Explorative Particle Swarm Optimization Method for Gas/Odor Source Localization in an Indoor Environment with no Strong Airflow

Ferri, Gabriele; Caselli, E.; Mattoli, Virgilio; Mondini, Alessio; Mazzolai, Barbara; Dario, P.

doi:10.1109/robio.2007.4522272

Cited by 13 publications

(10 citation statements)

References 17 publications

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“…The algorithm regards the odor source as the best solution, and takes each robot as a particle searching in the feasible region. The measured time‐averaged concentration [71] or some statistical indexes (e.g., the proximity index [54]) are used as the fitness function or the objective function. The S‐PSO method performed better than the multi‐robot gradient‐based search and biased‐random walk search.…”

Section: Heuristic Searching Methodsmentioning

confidence: 99%

Recent Progress and Trend of Robot Odor Source Localization

Jing

Meng

Ishida

2021

IEEJ Transactions Elec Engng

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Accurate and prompt localization of hazardous release sources ensures a timely emergency response to potential damages. Robot odor source localization is the study of locating the release source based on mobile robots in real‐time, which has been experiencing rapid growth in recent years. This paper presents a review of this field, focusing on recent progress and development trends. Research works are grouped according to method principles, leading to four categories: reactive methods, heuristic searching methods, probabilistic inference methods, and learning methods. The odor‐source direction and distance prediction via in situ sensing and developments of simulators are also listed separately for discussion. Several outlooks are highlighted at the end. Along with the developments of related fields and practical needs, we believe this field is bound to keep on flourishing in the future. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Heuristic Searching Methodsmentioning

confidence: 99%

Recent Progress and Trend of Robot Odor Source Localization

Jing

Meng

Ishida

2021

IEEJ Transactions Elec Engng

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“…Gas finding is the task of detecting an increased concentration of a target gas, gas source tracking is the task of following the cues determined from the sensed gas distribution towards the source and gas source declaration is the decision process finding out whether the source has been reached, at which location it is, or which object it is. Most of literature copes with gas source localization using bio-inspired approaches: for instance, in [11] a genetic algorithm is applied to a swarm of robots in order to locate the leakage of gas into an environment presenting a contained airflow. In [12], three bio-inspired odor source localization algorithms (casting, surge-spiral and surge-cast) are tested both in simulation and with real robots in a wind tunnel, and their expected performance is derived through a theoretical model.…”

Section: A Olfaction Mapsmentioning

confidence: 99%

Gas source localization in indoor environments using multiple inexpensive robots and stigmergy

Rocco¹,

Reggente²,

Saffiotti³

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Environmental monitoring is a rather new field in robotics. One of the main appealing tasks is gas mapping, i.e., the characterization of the chemical properties (concentration, dispersion, etc.) of the air within an environment. Current approaches rely on a robot using standard localization and mapping techniques to fuse gas measures with spatial features. These approaches require sophisticated sensors and/or high computational resources. We propose a minimalistic approach, in which one or multiple low-cost robots exploit the ability to store information in the environment, or "stigmergy", to effectively compute an artificial potential leading toward the likely location of the gas source, as indicated by a highest gas concentration or fluctuation. The potential is computed and stored directly on an array of RFID tags buried under the floor. Our approach has been validated in extensive experiments performed on real robots in a domestic environment.

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“…Although there was some reported success, one has to wonder the potential of distributed systems in solving this particularly complex problem. Previous researches has proposed a few algorithms to find and track plume, for example, Spiral-Surge [11] Ant Colony Algorithm [12], Particle Swarm Optimization [13] and their derivatives, some of these algorithms have limitations in terms of applicability to real robots. A robot swarm has always been lauded to be robust in the sense that it is unlikely to suffer from single-point failures.…”

Section: Introductionmentioning

confidence: 99%

Braitenberg swarm vehicles for odour plume tracking in laminar airflow

Zakaria

Kamarudin

Saad

et al. 2013

2013 IEEE Symposium on Computers &Amp; Informatics (ISCI)

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This paper presents an algorithm to trace an odour plume using swarm robots in laminar airflow. The algorithm proposed here aims to bridge the gap between single and multiple element systems by mimicking and enhancing biologically derived strategies for odor plume tracking. Simulations were carried out on Webots to verify the potential of the algorithm. A simple gas sensor model was introduced to mimic the response of a real metal oxide sensor in the simulation. A gas sensor model was introduced based on the response of metal oxide sensor (MOS) to closely mimic and provide real environment condition. Different weightage configurations of the gas sensor, k g and wind sensor, k w are compared to find its effects on the performance and behavior of the purposed algorithm. It was found that robots separated from the swarm can still perform the plume tracking task. Also, multiple entity systems show an increase in performance compared to single entity robots.

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Explorative Particle Swarm Optimization Method for Gas/Odor Source Localization in an Indoor Environment with no Strong Airflow

Cited by 13 publications

References 17 publications

Recent Progress and Trend of Robot Odor Source Localization

Recent Progress and Trend of Robot Odor Source Localization

Gas source localization in indoor environments using multiple inexpensive robots and stigmergy

Braitenberg swarm vehicles for odour plume tracking in laminar airflow

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