A Co-Operative Autonomous Offshore System for Target Detection Using Multi-Sensor Technology
This article studies the design, modeling, and implementation challenges for a target detection algorithm using multi-sensor technology of a co-operative autonomous offshore system, formed by an unmanned surface vehicle (USV) and an autonomous underwater vehicle (AUV). First, the study develops an a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/24/4106 |
| _version_ | 1797544549291130880 |
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| author | Jose Villa Jussi Aaltonen Sauli Virta Kari T. Koskinen |
| author_facet | Jose Villa Jussi Aaltonen Sauli Virta Kari T. Koskinen |
| author_sort | Jose Villa |
| collection | DOAJ |
| description | This article studies the design, modeling, and implementation challenges for a target detection algorithm using multi-sensor technology of a co-operative autonomous offshore system, formed by an unmanned surface vehicle (USV) and an autonomous underwater vehicle (AUV). First, the study develops an accurate mathematical model of the USV to be included as a simulation environment for testing the guidance, navigation, and control (GNC) algorithm. Then, a guidance system is addressed based on an underwater coverage path for the AUV, which uses a mechanical imaging sonar as the primary AUV perception sensor and ultra-short baseline (USBL) as a positioning system. Once the target is detected, the AUV sends its location to the USV, which creates a straight-line for path following with obstacle avoidance capabilities, using a LiDAR as the main USV perception sensor. This communication in the co-operative autonomous offshore system includes a decentralized Robot Operating System (ROS) framework with a master node at each vehicle. Additionally, each vehicle uses a modular approach for the GNC architecture, including target detection, path-following, and guidance control modules. Finally, implementation challenges in a field test scenario involving both AUV and USV are addressed to validate the target detection algorithm. |
| first_indexed | 2024-03-10T14:02:07Z |
| format | Article |
| id | doaj.art-76e6dc51100a46e6a88de3ffbf5b4cbf |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:02:07Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-76e6dc51100a46e6a88de3ffbf5b4cbf2023-11-21T01:00:16ZengMDPI AGRemote Sensing2072-42922020-12-011224410610.3390/rs12244106A Co-Operative Autonomous Offshore System for Target Detection Using Multi-Sensor TechnologyJose Villa0Jussi Aaltonen1Sauli Virta2Kari T. Koskinen3Mechatronics Research Group (MRG), Tampere University (TAU), 33720 Tampere, FinlandMechatronics Research Group (MRG), Tampere University (TAU), 33720 Tampere, FinlandAlamarin-Jet Oy, 62300 Härmä, FinlandMechatronics Research Group (MRG), Tampere University (TAU), 33720 Tampere, FinlandThis article studies the design, modeling, and implementation challenges for a target detection algorithm using multi-sensor technology of a co-operative autonomous offshore system, formed by an unmanned surface vehicle (USV) and an autonomous underwater vehicle (AUV). First, the study develops an accurate mathematical model of the USV to be included as a simulation environment for testing the guidance, navigation, and control (GNC) algorithm. Then, a guidance system is addressed based on an underwater coverage path for the AUV, which uses a mechanical imaging sonar as the primary AUV perception sensor and ultra-short baseline (USBL) as a positioning system. Once the target is detected, the AUV sends its location to the USV, which creates a straight-line for path following with obstacle avoidance capabilities, using a LiDAR as the main USV perception sensor. This communication in the co-operative autonomous offshore system includes a decentralized Robot Operating System (ROS) framework with a master node at each vehicle. Additionally, each vehicle uses a modular approach for the GNC architecture, including target detection, path-following, and guidance control modules. Finally, implementation challenges in a field test scenario involving both AUV and USV are addressed to validate the target detection algorithm.https://www.mdpi.com/2072-4292/12/24/4106target detectionco-operativeautonomousmulti-robotUSVAUV |
| spellingShingle | Jose Villa Jussi Aaltonen Sauli Virta Kari T. Koskinen A Co-Operative Autonomous Offshore System for Target Detection Using Multi-Sensor Technology Remote Sensing target detection co-operative autonomous multi-robot USV AUV |
| title | A Co-Operative Autonomous Offshore System for Target Detection Using Multi-Sensor Technology |
| title_full | A Co-Operative Autonomous Offshore System for Target Detection Using Multi-Sensor Technology |
| title_fullStr | A Co-Operative Autonomous Offshore System for Target Detection Using Multi-Sensor Technology |
| title_full_unstemmed | A Co-Operative Autonomous Offshore System for Target Detection Using Multi-Sensor Technology |
| title_short | A Co-Operative Autonomous Offshore System for Target Detection Using Multi-Sensor Technology |
| title_sort | co operative autonomous offshore system for target detection using multi sensor technology |
| topic | target detection co-operative autonomous multi-robot USV AUV |
| url | https://www.mdpi.com/2072-4292/12/24/4106 |
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