A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms
This article proposes a holistic localisation framework for underwater robotic swarms to dynamically fuse multiple position estimates of an autonomous underwater vehicle while using fuzzy decision support system. A number of underwater localisation methods have been proposed in the literature for wi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/19/5496 |
| _version_ | 1797552647675314176 |
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| author | Adham Sabra Wai-Keung Fung |
| author_facet | Adham Sabra Wai-Keung Fung |
| author_sort | Adham Sabra |
| collection | DOAJ |
| description | This article proposes a holistic localisation framework for underwater robotic swarms to dynamically fuse multiple position estimates of an autonomous underwater vehicle while using fuzzy decision support system. A number of underwater localisation methods have been proposed in the literature for wireless sensor networks. The proposed navigation framework harnesses the established localisation methods in order to provide navigation aids in the absence of acoustic exteroceptive sensors navigation aid (i.e., ultra-short base line) and it can be extended to accommodate newly developed localisation methods by expanding the fuzzy rule base. Simplicity, flexibility, and scalability are the main three advantages that are inherent in the proposed localisation framework when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. A physics-based simulation platform that considers environment’s hydrodynamics, industrial grade inertial measurement unit, and underwater acoustic communications characteristics is implemented in order to validate the proposed localisation framework on a swarm size of 150 autonomous underwater vehicles. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation by 16.53% and 35.17%, respectively, when compared to the Extended Kalman Filter based localisation with round-robin scheduling. |
| first_indexed | 2024-03-10T16:04:03Z |
| format | Article |
| id | doaj.art-ca709506feb940cca007abe4c85cb6f2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:04:03Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ca709506feb940cca007abe4c85cb6f22023-11-20T15:04:52ZengMDPI AGSensors1424-82202020-09-012019549610.3390/s20195496A Fuzzy Cooperative Localisation Framework for Underwater Robotic SwarmsAdham Sabra0Wai-Keung Fung1School of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, UKSchool of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, UKThis article proposes a holistic localisation framework for underwater robotic swarms to dynamically fuse multiple position estimates of an autonomous underwater vehicle while using fuzzy decision support system. A number of underwater localisation methods have been proposed in the literature for wireless sensor networks. The proposed navigation framework harnesses the established localisation methods in order to provide navigation aids in the absence of acoustic exteroceptive sensors navigation aid (i.e., ultra-short base line) and it can be extended to accommodate newly developed localisation methods by expanding the fuzzy rule base. Simplicity, flexibility, and scalability are the main three advantages that are inherent in the proposed localisation framework when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. A physics-based simulation platform that considers environment’s hydrodynamics, industrial grade inertial measurement unit, and underwater acoustic communications characteristics is implemented in order to validate the proposed localisation framework on a swarm size of 150 autonomous underwater vehicles. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation by 16.53% and 35.17%, respectively, when compared to the Extended Kalman Filter based localisation with round-robin scheduling.https://www.mdpi.com/1424-8220/20/19/5496underwater wireless sensor networksunderwater swarm roboticsautonomous underwater vehiclesunderwater localisationcooperative navigationfuzzy systems |
| spellingShingle | Adham Sabra Wai-Keung Fung A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms Sensors underwater wireless sensor networks underwater swarm robotics autonomous underwater vehicles underwater localisation cooperative navigation fuzzy systems |
| title | A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms |
| title_full | A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms |
| title_fullStr | A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms |
| title_full_unstemmed | A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms |
| title_short | A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms |
| title_sort | fuzzy cooperative localisation framework for underwater robotic swarms |
| topic | underwater wireless sensor networks underwater swarm robotics autonomous underwater vehicles underwater localisation cooperative navigation fuzzy systems |
| url | https://www.mdpi.com/1424-8220/20/19/5496 |
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