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Maritime Avoidance Navigation, Totally Integrated System (MANTIS)

Maritime Avoidance Navigation, Totally Integrated System (MANTIS)
Maritime Avoidance Navigation, Totally Integrated System (MANTIS)

The principal goal of marine navigation is for a ship to reach a destination point whilst avoiding other ships and keeping clear of non-navigable areas, such as shallow water and land, using Maritime Avoidance Navigation, Totally Integrated System (MANTIS). The functions of MANTIS are (C4): Compilation of navigation information using Data Fusion techniques, where a generic framework is implemented that incorporates stochastic error modelling methods with advance neurofuzzy techniques. A new variant of neurofuzzy network is introduced and appropriately labelled Transparent Reactive Online Neurofuzzy (TRON) network. Co-ordination of global vessel operations is handled automatically using a Vessel Management Expert System (VMES); capable of unambiguously solving complex multiple ship encounter scenarios as part of journey planning and en route collision avoidance. The resulting guidance way-points are efficient and takes into consideration non-navigable areas, collision avoidance regulations, ship characteristics, sea conditions, and sensor measurement accuracy. Control of ship dynamics is performed by an Autopilot for navigation precision employing developed steering and speed guidance laws, and a controller. Confirmation of current and future predicament and the validation of VMES solutions are made simple using an Integrated Display. Simulation results for each aspect of MANTIS exemplify the effectiveness of the complete system at tackling all the problems identified. In particular, the VMES demonstrator software through the functionality of its Graphical User Interface (GUI) makes the validation process intuitive and simple, and instils confidence in the system capabilities.

University of Southampton
Tran, Tien
c082b815-9528-4aac-8436-e3359ed7b3c1
Tran, Tien
c082b815-9528-4aac-8436-e3359ed7b3c1

Tran, Tien (2001) Maritime Avoidance Navigation, Totally Integrated System (MANTIS). University of Southampton, Doctoral Thesis.

Record type: Thesis (Doctoral)

Abstract

The principal goal of marine navigation is for a ship to reach a destination point whilst avoiding other ships and keeping clear of non-navigable areas, such as shallow water and land, using Maritime Avoidance Navigation, Totally Integrated System (MANTIS). The functions of MANTIS are (C4): Compilation of navigation information using Data Fusion techniques, where a generic framework is implemented that incorporates stochastic error modelling methods with advance neurofuzzy techniques. A new variant of neurofuzzy network is introduced and appropriately labelled Transparent Reactive Online Neurofuzzy (TRON) network. Co-ordination of global vessel operations is handled automatically using a Vessel Management Expert System (VMES); capable of unambiguously solving complex multiple ship encounter scenarios as part of journey planning and en route collision avoidance. The resulting guidance way-points are efficient and takes into consideration non-navigable areas, collision avoidance regulations, ship characteristics, sea conditions, and sensor measurement accuracy. Control of ship dynamics is performed by an Autopilot for navigation precision employing developed steering and speed guidance laws, and a controller. Confirmation of current and future predicament and the validation of VMES solutions are made simple using an Integrated Display. Simulation results for each aspect of MANTIS exemplify the effectiveness of the complete system at tackling all the problems identified. In particular, the VMES demonstrator software through the functionality of its Graphical User Interface (GUI) makes the validation process intuitive and simple, and instils confidence in the system capabilities.

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Published date: 2001

Identifiers

Local EPrints ID: 464503
URI: http://eprints.soton.ac.uk/id/eprint/464503
PURE UUID: 89e9d389-97e6-4b7f-a178-ae1bce9a7fd8

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Date deposited: 04 Jul 2022 23:42
Last modified: 16 Mar 2024 19:34

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Contributors

Author: Tien Tran

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