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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shaukat, M. Chitre, M. Sim Heng Ong |
| Copyright Year | 2013 |
| Description | Author affiliation: NUS Grad. Sch. for Integrative Sci. & Eng., Nat. Univ. of Singapore, Singapore, Singapore (Shaukat, M.) || Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore (Chitre, M.; Sim Heng Ong) |
| Abstract | We present a bio-inspired distributed algorithm which is a fusion of two distinct animal behaviours to solve three different underwater search missions. One of the two constituent control modules is called target-drive which models the hypothesized behaviour of a fish-larva searching for a coral reef using acoustic cues. Target-drive helps a single Autonomous Underwater Vehicle (AUV) to adjust its heading towards the acoustic source. The other control module called group-cohesion, mimics movements of a golden shiner (Notemigonus crysoleucas) in a school of fish. The proposed approach only relies on implicit communication and achieves target convergence only by assuming a single on-board hydrophone and location estimate of AUV's neighbours. The effects of varying key parameters such as group-size and neighbourhood-radius on convergence times have been thoroughly investigated. We present a preliminary analysis of the algorithm's performance which shows promise in solving problems employing small teams of AUVs in terms of convergence times and non-existent inter-agent communication. |
| Starting Page | 1 |
| Ending Page | 10 |
| File Size | 1145898 |
| Page Count | 10 |
| File Format | |
| e-ISBN | 9781479900015 |
| DOI | 10.1109/OCEANS-Bergen.2013.6607954 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-10 |
| Publisher Place | Norway |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Force Convergence Sensors Educational institutions Underwater acoustics Robots |
| Content Type | Text |
| Resource Type | Article |
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