A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective
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 co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/10/2231 |
| _version_ | 1828115964006236160 |
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| author | João Macedo Lino Marques Ernesto Costa |
| author_facet | João Macedo Lino Marques Ernesto Costa |
| author_sort | João Macedo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T12:49:51Z |
| format | Article |
| id | doaj.art-6fa80b494b3a4d7680aeeaf01352e39e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:49:51Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6fa80b494b3a4d7680aeeaf01352e39e2022-12-22T04:23:15ZengMDPI AGSensors1424-82202019-05-011910223110.3390/s19102231s19102231A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action PerspectiveJoão Macedo0Lino Marques1Ernesto Costa2Institute of Systems and Robotics, University of Coimbra, 3030-290 Coimbra, PortugalInstitute of Systems and Robotics, University of Coimbra, 3030-290 Coimbra, PortugalCentre for Informatics and Systems of the University of Coimbra, 3030-290 Coimbra, PortugalLocating 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.https://www.mdpi.com/1424-8220/19/10/2231odour source localisationmobile robot olfactionbio-inspired strategies |
| spellingShingle | João Macedo Lino Marques Ernesto Costa A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective Sensors odour source localisation mobile robot olfaction bio-inspired strategies |
| title | A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective |
| title_full | A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective |
| title_fullStr | A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective |
| title_full_unstemmed | A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective |
| title_short | A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective |
| title_sort | comparative study of bio inspired odour source localisation strategies from the state action perspective |
| topic | odour source localisation mobile robot olfaction bio-inspired strategies |
| url | https://www.mdpi.com/1424-8220/19/10/2231 |
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