Neural Network Architecture for Cognitive Navigation in Dynamic Environments

Navigation in time-evolving environments with moving targets and obstacles requires cognitive abilities widely demonstrated by even simplest animals. However, it is a long-standing challenging problem for artificial agents. Cognitive autonomous robots coping with this problem must solve two essentia...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 24; no. 12; pp. 2075 - 2087
Main Authors: Villacorta-Atienza, Jose Antonio, Makarov, Valeri A.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.12.2013
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Animals
Applied sciences
Artificial intelligence
Biological neural networks
Biomimetics - methods
Cognition
Cognition - physiology
Cognitive ability
cognitive maps
Computer science; control theory; systems
Connectionism. Neural networks
Control theory. Systems
Decision Support Techniques
dynamical systems
Exact sciences and technology
Humans
internal representation
Motion
Navigation
Neural networks
Neural Networks (Computer)
Pattern Recognition, Automated - methods
Robot sensing systems
Robotics
Robotics - methods
Robots
Shape
Spatial Navigation
Training
Trajectory
Autonomous system
Closed loop
Obstacle
Moving target
Navigation
cognitive maps
Mental representation
Moving robot
Decision making
Cognitive ability
neural networks
Network architecture
internal representation
Cognition
Neural network
Dynamical system
Robotics
Cognitive map
Cognitive theory
dynamical systems
Uncertain system
Animal
Information structure
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Navigation in time-evolving environments with moving targets and obstacles requires cognitive abilities widely demonstrated by even simplest animals. However, it is a long-standing challenging problem for artificial agents. Cognitive autonomous robots coping with this problem must solve two essential tasks: 1) understand the environment in terms of what may happen and how I can deal with this and 2) learn successful experiences for their further use in an automatic subconscious way. The recently introduced concept of compact internal representation (CIR) provides the ground for both the tasks. CIR is a specific cognitive map that compacts time-evolving situations into static structures containing information necessary for navigation. It belongs to the class of global approaches, i.e., it finds trajectories to a target when they exist but also detects situations when no solution can be found. Here we extend the concept of situations with mobile targets. Then using CIR as a core, we propose a closed-loop neural network architecture consisting of conscious and subconscious pathways for efficient decision-making. The conscious pathway provides solutions to novel situations if the default subconscious pathway fails to guide the agent to a target. Employing experiments with roving robots and numerical simulations, we show that the proposed architecture provides the robot with cognitive abilities and enables reliable and flexible navigation in realistic time-evolving environments. We prove that the subconscious pathway is robust against uncertainty in the sensory information. Thus if a novel situation is similar but not identical to the previous experience (because of, e.g., noisy perception) then the subconscious pathway is able to provide an effective solution.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2013.2271645