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towards a neuroinspired navigation system for robot planning
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Tang
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Guangzhi
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Guangzhi Tang
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author
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chair
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Metaxas
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Dimitris
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Dimitris Metaxas
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internal member
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Bekris
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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theses
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2017
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2017-05
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2017
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xx
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eng
Abstract (type = abstract)
The ability to orient in an unknown, fast-changing, environment is an unmet challenge for robots but a seamlessly solved problem for the primate brain. This thesis describes the first steps in developing a neuro-inspired “bottom-up” model of the brain’s navigation system to make a mobile robot localize itself, map its surrounding and plan its trajectory. Our model employs neural spikes to encode and process information in real-time. Despite a multitude of Nobel-winning studies that have revealed neurons specializing in self-navigation, such as place, grid, border and head direction cells, their interconnectivity remains elusive. Therefore, any model employing these neurons needs to make quite a lot of extrapolations to fill in the gaps of knowledge. The main challenge was to design a real-time spiking neural network that can compensate for the hardware limitations as well as its own intrinsic imperfections and work in real conditions. To design the first component of our model, the head direction cell layer, we employed mechanisms based on self-organizing and self-sustaining neural activity, or attractor dynamics, resembling those originally proposed in Hebb’s cell assembly theory. The information to be maintained and updated was a continuous variable, or continuous attractor, where a 1D continuum of cell assemblies represented head direction. In theory, our network should give rise to a self-sustained hill of excitation - the attractor. In practice, due to non-ideal speed sensors and the intrinsic spike variability of the spiking network, it was impossible to sustain a correct approximation of the head direction using just this scheme. To correct this, we introduced a spike-based Bayesian inference layer of leaky-integrate-and-fire models of neurons, that combined feedforward (vision) and recursive (kinesthetic) inputs. We show how such a layer can approximate the posterior probability of the preferred state encoded in the spiking probability by adding the logarithms of the simulated dendritic currents, which is a reasonable approximation of the nonlinear dendritic activity. We show that our model accurately estimated head direction and further extend it to include a dynamic network of border cells that can learn to map the observed environment through simulating synaptic plasticity. Solving the localization problem and creating a cognitive map of the surroundings, our thesis paves the way for tackling robot planning through imitating brain structure, its principles and its performance.
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Topic
Computer Science
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Rutgers University Electronic Theses and Dissertations
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ETD_8030
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electronic resource
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Supplementary File: Latex source of the pdf
Extent
1 online resource (viii, 40 p. : ill.)
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Intelligent control systems
Subject (authority = ETD-LCSH)
Topic
Robots
Subject (authority = ETD-LCSH)
Topic
Space perception
Note (type = statement of responsibility)
by Guangzhi Tang
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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Identifier (type = doi)
doi:10.7282/T3Z03C3T
Genre (authority = ExL-Esploro)
ETD graduate
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The author owns the copyright to this work.
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Name
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Tang
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Guangzhi
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Permission or license
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2017-04-14 00:40:12
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Guangzhi Tang
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Rutgers University. Graduate School - New Brunswick
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Copyright protected
Availability
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Open
Reason
Permission or license
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