Odor source localization algorithms on mobile robots: A review and future outlook

Chen, Xinxing; Huang, Jian

doi:10.1016/j.robot.2018.11.014

Cited by 168 publications

(78 citation statements)

References 70 publications

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“…The studies presented here cover two important sensing modalities for animal behavior—olfaction and audition. It is important to note that the robotics community has developed a number of analytical approaches for chemotaxis (see Chen and Huang, 2019 for a recent review) and phonotaxis (Huang et al, 1999 ; Bicho et al, 2000 ; Andersson et al, 2004 ; Zu et al, 2009 ; Zuojun et al, 2012 ; Hwang et al, 2014 ). Conventional robot chemotaxis approaches are either gradient-based (see Kowadlo and Russell, 2008 ; Ishida et al, 2012 for a review) or probabilistic and map-based, such as infotaxis (Vergassola et al, 2007 ), Bayesian inference (Wei Li et al, 2006 ), Kalman filtering (Blanco et al, 2013 ), particle filtering (Li et al, 2011 ), spatial memory-based behaviors (Grünbaum and Willis, 2015 ), Hidden Markov Models (Farrell et al, 2003 ), and kernel methods (Lilienthal et al, 2009 ; Reggente and Lilienthal, 2010 ).…”

Section: Discussionmentioning

confidence: 99%

Braitenberg Vehicles as Computational Tools for Research in Neuroscience

Shaikh

Rañó

2020

Front. Bioeng. Biotechnol.

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Section: Discussionmentioning

confidence: 99%

Braitenberg Vehicles as Computational Tools for Research in Neuroscience

Shaikh

Rañó

2020

Front. Bioeng. Biotechnol.

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“…In environments where the air flow is weak or non-existent, biological organisms employ chemotactic strategies, which only use information about the chemical gradient. On the other hand, in environments containing a strong air flow, animals typically take advantage of the flow information to guide their search process [7,8]. It has been hypothesised that bio-inspired strategies may not be able to succeed in the real-world, as the existing sensors and robots are less capable than their biological counterparts [9].…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective

Macedo

Marques

Costa

2019

Sensors

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Locating odour sources with robots is an interesting problem with many important real-world applications. In the past years, the robotics community has adapted several bio-inspired strategies to search for odour sources in a variety of environments. This work studies and compares some of the most common strategies from a behavioural perspective with the aim of knowing: (1) how different are the behaviours exhibited by the strategies for the same perceptual state; and (2) which are the most consensual actions for each perceptual state in each environment. The first step of this analysis consists of clustering the perceptual states, and building histograms of the actions taken for each cluster. In case of (1), a histogram is made for each strategy separately, whereas for (2), a single histogram containing the actions of all strategies is produced for each cluster of states. Finally, statistical hypotheses tests are used to find the statistically significant differences between the behaviours of the strategies in each state. The data used for performing this study was gathered from a purpose-built simulator which accurately simulates the real-world phenomena of odour dispersion and air flow, whilst being sufficiently fast to be employed in learning and evolutionary robotics experiments. This paper also proposes an xml-inspired structure for the generated datasets that are used to store the perceptual information of the robots over the course of the simulations. These datasets may be used in learning experiments to estimate the quality of a candidate solution or for measuring its novelty.

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“…We can divide the existing odor source localization algorithms for single robots into three categories [4], [5]: gradientbased algorithms, bio-inspired algorithms and probabilistic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Particle Source Localization With a Low-Cost Robotic Sensor System: Algorithmic Design and Performance Evaluation

et al. 2020

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In this paper we tackle the problem of finding the source of particulate matter with a mobile robot equipped with a low-cost multi-channel optical particle counting sensor. The proposed method is based on the Infotaxis odor source localization algorithm and makes multiple modifications to adapt it to particle plumes. In particular, we propose three simple but efficient ways to fuse multiple probability maps associated with various particle sizes and use the resulted integrated map to guide the robot's movements. A refined measurement data collection is conducted in a wind tunnel to fit the particle plume model. The method with three proposed integration strategies is evaluated in simulation and in the wind tunnel emulating realistic environmental conditions in a repeatable fashion. In particular, we have investigated the impact of two environmental parameters-the wind speed and source release rate on the algorithm performance. The proposed algorithm with the weighted multi-modality map integration strategy outperforms the original Infotaxis and the other two variants. In high wind speed, the proposed algorithm is able on average to estimate the source location with less than 1 meter error in the 80 m 2 wind tunnel arena.

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Odor source localization algorithms on mobile robots: A review and future outlook

Cited by 168 publications

References 70 publications

Braitenberg Vehicles as Computational Tools for Research in Neuroscience

Braitenberg Vehicles as Computational Tools for Research in Neuroscience

A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective

Particle Source Localization With a Low-Cost Robotic Sensor System: Algorithmic Design and Performance Evaluation

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