Path reconstruction in diffusion tensor magnetic resonance imaging

The complicated underwater environment and the poor underwater vision make super-mini underwater cable robot hardly to be controlled. Traditionally, the manual control method by operators is adopted by this kind of robots. Unfortunately, the robots can hardly work normally in these practical circumstances. Therefore, to overcome these shortcomings and improve the abilities of these underwater cable robots, this paper proposes several improvements, including the system design, the motion controller design, three dimensional obstacle recognition and three dimensional path reconstruction technologies etc. The details are displayed as follow: (1) Super-mini underwater robot system design: several improvement schemes and important design ideas are investigated for the super-mini underwater robot.(2) Super-mini robot motion controller design: The motion controller design of underwater robot in complicated circumstance is investigated. A new adaptive neural network sliding mode controller with balanced parameter controller (ANNSMB) is proposed. Based on the theory of adaptive fuzzy sliding mode controller (AFSMC), an improved algorithm is also proposed and applied to the underwater robot. (3)Research of three dimensional underwater environment reconstructions: The algorithms and the experiments of underwater environment reconstructions are investigated. DT-MRI image processing algorithm and the theory of three dimensional obstacle reconstructions are adopted and improved for the application of the underwater robot. (4) The super-mini underwater robot path planning algorithms are investigated.

Data and Resources

Additional Info

Field Value
Source https://theses.hal.science/tel-00694403
Author Song, Xin
Maintainer CCSD
Last Updated May 19, 2026, 22:09 (UTC)
Created May 19, 2026, 22:09 (UTC)
Identifier NNT: 2011ISAL0066
Language en
Rights https://about.hal.science/hal-authorisation-v1/
contributor Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE) ; Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS) ; Université Claude Bernard Lyon 1 (UCBL) ; Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon) ; Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM) ; Université Jean Monnet (EPSCPE) (UJM EPE)-Université Jean Monnet (EPSCPE) (UJM EPE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL) ; Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon) ; Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM) ; Université Jean Monnet (EPSCPE) (UJM EPE)-Université Jean Monnet (EPSCPE) (UJM EPE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
creator Song, Xin
date 2011-07-13T00:00:00
harvest_object_id 53413683-a90d-4f64-9f5d-f0b35078a0b7
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-04-23T00:00:00
set_spec type:THESE