Probability-PSO Algorithm for Multi-robot Based Odor Source Localization in Ventilated Indoor Environments

Li, Fēi; Meng, Qinghao; Bai, Shuang; Li, Ji-Gong; Popescu, Dorin

doi:10.1007/978-3-540-88513-9_128

Cited by 22 publications

(13 citation statements)

References 10 publications

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“…An alternate name for OSL, chemical plume tracing (Farrell et al 2005;Zarzhitsky et al 2005), reflects the importance of the plumetracing strategy in these methods. Some biologically inspired approaches have been designed for plume tracing, such as gradient-following-based algorithms (Grasso et al 2000;Holland and Melhuish 1996;Sandini et al 1993) (intended to mimic the behavior of chemotaxis), upwind algorithms (Hayes et al 2003;Ishida et al 1994;Russell et al 1995Russell et al , 2003 (intended to mimic the behavior of anemotaxis), the SPIRAL algorithm in an indoor environment with no strong airflow (Ferri et al 2009), and swarm-based algorithms (Hayes et al 2003;Li et al 2008;Marques et al 2006;Zarzhitsky et al 2005) (taking their inspiration from social insects). Moreover, some engineered plume-tracing strategies have also been proposed, such as the fluxotaxis (Zarzhitsky et al 2005) and infotaxis (Vergassola et al 2007) algorithms.…”

Section: Introductionmentioning

confidence: 99%

Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm

et al. 2011

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This paper discusses odor source localization (OSL) using a mobile robot in an outdoor time-variant airflow environment. A novel OSL algorithm based on particle filters (PF) is proposed. When the odor plume clue is found, the robot performs an exploratory behavior, such as a plume-tracing strategy, to collect more information about the previously unknown odor source. In parallel, the information collected by the robot is exploited by the PF-based OSL algorithm to estimate the location of the odor source in real time. The process of the OSL is terminated if the estimated source locations converge within a given small area. The Bayesian-inference-based method is also performed for comparison. Experimental results indicate that the proposed PF-based OSL algorithm performs better than the Bayesianinference-based OSL method.

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Section: Introductionmentioning

confidence: 99%

Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm

et al. 2011

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“…As far as the cooperative multi-agent source localization approaches are concerned, they are either scaled versions of the individualistic behavior (Li, Meng, Bai, Li, & Popescu, 2008) or require explicit inter-agent communication for social behaviors (Marjovi, Nunes, Sousa, Faria, & Marques, 2010) to achieve cooperation. This is also true for the single-sensor based temporal sampling implementations which either require centralized (Ogren, Fiorelli, & Leonard, 2004) or decentralized explicit inter-agent communication (Bachmayer & Leonard, 2002; Shaukat et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

Adaptive behaviors in multi-agent source localization using passive sensing

Shaukat¹

2016

Adaptive Behavior

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In this paper, the role of adaptive group cohesion in a cooperative multi-agent source localization problem is investigated. A distributed source localization algorithm is presented for a homogeneous team of simple agents. An agent uses a single sensor to sense the gradient and two sensors to sense its neighbors. The algorithm is a set of individualistic and social behaviors where the individualistic behavior is as simple as an agent keeping its previous heading and is not self-sufficient in localizing the source. Source localization is achieved as an emergent property through agent’s adaptive interactions with the neighbors and the environment. Given a single agent is incapable of localizing the source, maintaining team connectivity at all times is crucial. Two simple temporal sampling behaviors, intensity-based-adaptation and connectivity-based-adaptation, ensure an efficient localization strategy with minimal agent breakaways. The agent behaviors are simultaneously optimized using a two phase evolutionary optimization process. The optimized behaviors are estimated with analytical models and the resulting collective behavior is validated against the agent’s sensor and actuator noise, strong multi-path interference due to environment variability, initialization distance sensitivity and loss of source signal.

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“…Several other methods have been proposed for plume tracking using swarm robotic concepts, namely, biasing expansion swarm approach (BESA) [12], biased random walk (BRW) [13], particle swarm optimization (PSO) [14], [15], glowworm swarm optimization (GSO) [16], gradient climbing techniques, swarm spiral surge [17], and physicsbased swarming approach [18]. Researchers have developed methods that employ combinations and variations of plume acquisition and plume upwind following [19] using reactive control algorithms (comparisons of these kinds of methods The black signal in A shows the instantaneous measurements of a fast gas sensor while moving cross-wind, the red signal in B shows the output of a slow sensor (that acts like a low-pass filter) that moves cross-wind, the green signal in C shows the average of the measurements during a long time period.…”

Section: Introductionmentioning

confidence: 99%

Swarm robotic plume tracking for intermittent and time-variant odor dispersion

Marjovi

Marques

2013

2013 European Conference on Mobile Robots

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This paper presents a method for odor plume tracking by a swarm of robots in realistic conditions. In real world environments, the chemical concentration within an odor plume is patchy, intermittent and time-variant. This study shows that swarm robots can cooperatively track the odor plume towards its source by establishing a cohesive spatial sensor network to deal with the turbulences and patchy nature of odor plumes. The robots move together and maintain a distance margin between themselves in order to keep the cohesion of the constructed sensor network while the odor concentration and air-flow speed are considered in the equations of navigation of the robots in the network to more efficiently track the plume. The method is evaluated in simulation against various number of robots, the emission rate of the odor source, the number of obstacles in the environment and the size of the testing environment. The emergent behavior of the swarm proves the functionality, robustness and scalability of the system in different conditions.

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Probability-PSO Algorithm for Multi-robot Based Odor Source Localization in Ventilated Indoor Environments

Cited by 22 publications

References 10 publications

Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm

Odor source localization using a mobile robot in outdoor airflow environments with a particle filter algorithm

Adaptive behaviors in multi-agent source localization using passive sensing

Swarm robotic plume tracking for intermittent and time-variant odor dispersion

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