Development of Deterministic Artificial Intelligence for Unmanned Underwater Vehicles (UUV)
The major premise of deterministic artificial intelligence (D.A.I.) is to assert deterministic self-awareness statements based in either the physics of the underlying problem or system identification to establish governing differential equations. The key distinction between D.A.I. and ubiquitous sto...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/8/8/578 |
| _version_ | 1797560587494883328 |
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| author | Timothy Sands |
| author_facet | Timothy Sands |
| author_sort | Timothy Sands |
| collection | DOAJ |
| description | The major premise of deterministic artificial intelligence (D.A.I.) is to assert deterministic self-awareness statements based in either the physics of the underlying problem or system identification to establish governing differential equations. The key distinction between D.A.I. and ubiquitous stochastic methods for artificial intelligence is the adoption of first principles whenever able (in every instance available). One benefit of applying artificial intelligence principles over ubiquitous methods is the ease of the approach once the re-parameterization is derived, as done here. While the method is deterministic, researchers need only understand linear regression to understand the optimality of both self-awareness and learning. The approach necessitates full (autonomous) expression of a desired trajectory. Inspired by the exponential solution of ordinary differential equations and Euler’s expression of exponential solutions in terms of sinusoidal functions, desired trajectories will be formulated using such functions. Deterministic self-awareness statements, using the autonomous expression of desired trajectories with buoyancy control neglected, are asserted to control underwater vehicles in ideal cases only, while application to real-world deleterious effects is reserved for future study due to the length of this manuscript. In totality, the proposed methodology automates control and learning merely necessitating very simple user inputs, namely desired initial and final states and desired initial and final time, while tuning is eliminated completely. |
| first_indexed | 2024-03-10T18:02:49Z |
| format | Article |
| id | doaj.art-0fb60fecf4084b1ab1e02f922cb4c4e5 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T18:02:49Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-0fb60fecf4084b1ab1e02f922cb4c4e52023-11-20T08:43:32ZengMDPI AGJournal of Marine Science and Engineering2077-13122020-07-018857810.3390/jmse8080578Development of Deterministic Artificial Intelligence for Unmanned Underwater Vehicles (UUV)Timothy Sands0Department of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14850, USAThe major premise of deterministic artificial intelligence (D.A.I.) is to assert deterministic self-awareness statements based in either the physics of the underlying problem or system identification to establish governing differential equations. The key distinction between D.A.I. and ubiquitous stochastic methods for artificial intelligence is the adoption of first principles whenever able (in every instance available). One benefit of applying artificial intelligence principles over ubiquitous methods is the ease of the approach once the re-parameterization is derived, as done here. While the method is deterministic, researchers need only understand linear regression to understand the optimality of both self-awareness and learning. The approach necessitates full (autonomous) expression of a desired trajectory. Inspired by the exponential solution of ordinary differential equations and Euler’s expression of exponential solutions in terms of sinusoidal functions, desired trajectories will be formulated using such functions. Deterministic self-awareness statements, using the autonomous expression of desired trajectories with buoyancy control neglected, are asserted to control underwater vehicles in ideal cases only, while application to real-world deleterious effects is reserved for future study due to the length of this manuscript. In totality, the proposed methodology automates control and learning merely necessitating very simple user inputs, namely desired initial and final states and desired initial and final time, while tuning is eliminated completely.https://www.mdpi.com/2077-1312/8/8/578UUVAUVROVhydrodynamicsnavigation systemdynamic positioning |
| spellingShingle | Timothy Sands Development of Deterministic Artificial Intelligence for Unmanned Underwater Vehicles (UUV) Journal of Marine Science and Engineering UUV AUV ROV hydrodynamics navigation system dynamic positioning |
| title | Development of Deterministic Artificial Intelligence for Unmanned Underwater Vehicles (UUV) |
| title_full | Development of Deterministic Artificial Intelligence for Unmanned Underwater Vehicles (UUV) |
| title_fullStr | Development of Deterministic Artificial Intelligence for Unmanned Underwater Vehicles (UUV) |
| title_full_unstemmed | Development of Deterministic Artificial Intelligence for Unmanned Underwater Vehicles (UUV) |
| title_short | Development of Deterministic Artificial Intelligence for Unmanned Underwater Vehicles (UUV) |
| title_sort | development of deterministic artificial intelligence for unmanned underwater vehicles uuv |
| topic | UUV AUV ROV hydrodynamics navigation system dynamic positioning |
| url | https://www.mdpi.com/2077-1312/8/8/578 |
| work_keys_str_mv | AT timothysands developmentofdeterministicartificialintelligenceforunmannedunderwatervehiclesuuv |