Transition Cells and Neural Fields for Navigation and Planning

Cuperlier, Nicolas; Quoy, Mathias; Laroque, Philippe; Gaussier, Philippe

doi:10.1007/11499220_36

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…In order to allow their integration, the action layer is implemented as a neural field. A neural fields model has been seen as a simple but effective way to model motion perception (Giese, 1998), and has drawn more attention in the robotic area because of its distributed representation and the dynamic integration of information (Cuperlier et al, 2005; Toussaint, 2006; Torta et al, 2011). For example, Erlhagen and Bicho (2006) achieved a goal-directed robot navigation behavior with real-time obstacle avoidance with dynamic neural fields (DNFs).…”

Section: Related Workmentioning

confidence: 99%

“…The DNF is a biologically-inspired model of the neural dynamics in cortical tissues (Amari, 1977), which is of interest in robotics to generate dynamic behavior (Cuperlier et al, 2005; Erlhagen and Bicho, 2006). A one-dimensional ring-form DNF with 36 neurons is implemented in our work representing the desired robot orientation in 10° increments.…”

Section: Architecturementioning

confidence: 99%

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Learning indoor robot navigation using visual and sensorimotor map information

2013

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As a fundamental research topic, autonomous indoor robot navigation continues to be a challenge in unconstrained real-world indoor environments. Although many models for map-building and planning exist, it is difficult to integrate them due to the high amount of noise, dynamics, and complexity. Addressing this challenge, this paper describes a neural model for environment mapping and robot navigation based on learning spatial knowledge. Considering that a person typically moves within a room without colliding with objects, this model learns the spatial knowledge by observing the person's movement using a ceiling-mounted camera. A robot can plan and navigate to any given position in the room based on the acquired map, and adapt it based on having identified possible obstacles. In addition, salient visual features are learned and stored in the map during navigation. This anchoring of visual features in the map enables the robot to find and navigate to a target object by showing an image of it. We implement this model on a humanoid robot and tests are conducted in a home-like environment. Results of our experiments show that the learned sensorimotor map masters complex navigation tasks.

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Section: Related Workmentioning

confidence: 99%

Section: Architecturementioning

confidence: 99%

Learning indoor robot navigation using visual and sensorimotor map information

2013

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“…In the first case, there are sudden changes in the movements when a new neuron wins. In the second one, the final movement is a combination of different transitions (Cuperlier et al, 2005 ). Thus, at a given location several neurons in ECs are firing.…”

Section: Our Navigation Architecturementioning

confidence: 99%

Neurobiologically inspired mobile robot navigation and planning

2007

Self Cite

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After a short review of biologically inspired navigation architectures, mainly relying on modeling the hippocampal anatomy, or at least some of its functions, we present a navigation and planning model for mobile robots. This architecture is based on a model of the hippocampal and prefrontal interactions. In particular, the system relies on the definition of a new cell type "transition cells" that encompasses traditional "place cells".

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“…Due to the distinguishing characteristics of the proposed VNR approach, there is limited research work similar to ours. Some groups have reported success in navigational tasks using neuromorphic architectures (Banquet et al 2005 ; Cuperlier et al, 2005 ; Krichmar et al, 2005 ; Ogata et al, 2004 ; Wiener and Arleo, 2003 ). Notable endeavors that share similarities with our work include identification of challenges and opportunities in robot-mediated neurorehabilitation (Harwin et al, 2006 ), development of prototypes that combine robotics and virtual reality to assist in the rehabilitation of brain-injured patients and support motor control research (Patton et al, 2006 ), and generation of artificial brains for virtual robots using a new paradigm based on the epigenetic approach (Pasquier, 2004 , 2005 ).…”

Section: Related Workmentioning

confidence: 99%

Framework and implications of virtual neurorobotics

Goodman

Zou²,

Dascalu³

2008

Front. Neurosci.

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Despite decades of societal investment in artifi cial learning systems, truly "intelligent" systems have yet to be realized. These traditional models are based on input-output pattern optimization and/or cognitive production rule modeling. One response has been social robotics, using the interaction of human and robot to capture important cognitive dynamics such as cooperation and emotion; to date, these systems still incorporate traditional learning algorithms. More recently, investigators are focusing on the core assumptions of the brain "algorithm" itself-trying to replicate uniquely "neuromorphic" dynamics such as action potential spiking and synaptic learning. Only now are large-scale neuromorphic models becoming feasible, due to the availability of powerful supercomputers and an expanding supply of parameters derived from research into the brain's interdependent electrophysiological, metabolomic and genomic networks. Personal computer technology has also led to the acceptance of computer-generated humanoid images, or "avatars", to represent intelligent actors in virtual realities. In a recent paper, we proposed a method of virtual neurorobotics (VNR) in which the approaches above (social-emotional robotics, neuromorphic brain architectures, and virtual reality projection) are hybridized to rapidly forward-engineer and develop increasingly complex, intrinsically intelligent systems. In this paper, we synthesize our research and related work in the fi eld and provide a framework for VNR, with wider implications for research and practical applications.

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Transition Cells and Neural Fields for Navigation and Planning

Cited by 7 publications

References 19 publications

Learning indoor robot navigation using visual and sensorimotor map information

Learning indoor robot navigation using visual and sensorimotor map information

Neurobiologically inspired mobile robot navigation and planning

Framework and implications of virtual neurorobotics

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