Neural network based FastSLAM for autonomous robots in unknown environments

2015 ◽  
Vol 165 ◽  
pp. 99-110 ◽  
Author(s):  
Qing-Ling Li ◽  
Yu Song ◽  
Zeng-Guang Hou
Keyword(s):  
Neural Network ◽  
Autonomous Robots ◽  
Unknown Environments

Control of Autonomous Robots Using the Principles of Neuromodulation

2013 ◽  
Author(s):  
Akimul Prince ◽  
Biswanath Samanta
Keyword(s):  
Neural Network ◽  
Autonomous Robots ◽  
Neuronal Model ◽  
Autonomous Robot ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Control Approach ◽  
The Neural Network ◽  
Simple Neural Network ◽  
Interesting Behavior

The paper presents a control approach based on vertebrate neuromodulation and its implementation on an autonomous robot platform. A simple neural network is used to model the neuromodulatory function for generating context based behavioral responses to sensory signals. The neural network incorporates three types of neurons — cholinergic and noradrenergic (ACh/NE) neurons for attention focusing and action selection, dopaminergic (DA) neurons for curiosity-seeking, and serotonergic (5-HT) neurons for risk aversion behavior. The implementation of the neuronal model on a relatively simple autonomous robot illustrates its interesting behavior adapting to changes in the environment. The integration of neuromodulation based robots in the study of human-robot interaction would be worth considering in future.

Neuromodulation Based Control of Autonomous Robots

2013 ◽  
Author(s):  
Akimul Prince ◽  
Biswanath Samanta
Keyword(s):  
Neural Network ◽  
Sensory Input ◽  
Autonomous Robots ◽  
Behavioral Responses ◽  
Neuronal Model ◽  
Robotic Platform ◽  
Control Approach ◽  
The Neural Network ◽  
Input Signals ◽  
Interesting Behavior

The paper presents a control approach based on neuromodulation in vertebrate brains and its implementation on an autonomous robotic platform. The neuromodulatory function is modeled through a neural network for generating context based behavioral responses to sensory input signals from the environment. Three types of neurons are incorporated in the neural network model. The neurons are — cholinergic and noradrenergic (ACh/NE) for attention focusing and action selection, dopaminergic (DA) for curiosity-seeking, and serotonergic (5-HT) for risk aversion behaviors. The neuronal model was implemented on a relatively simple autonomous robot that demonstrated its interesting behavior adapting to changes in the environment.

scholarly journals A Behaviour-Based Architecture for Mapless Navigation Using Vision

10.5772/46200 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 18 ◽  
Author(s):  
Mehmet Serdar Guzel ◽  
Robert Bicker
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Autonomous Robots ◽  
Monocular Vision ◽  
Challenging Problem ◽  
Simulation Environment ◽  
Dynamic Changes ◽  
Cluttered Environment ◽  
Navigation Strategy ◽  
Novel Method

Autonomous robots operating in an unknown and uncertain environment must be able to cope with dynamic changes to that environment. For a mobile robot in a cluttered environment to navigate successfully to a goal while avoiding obstacles is a challenging problem. This paper presents a new behaviour-based architecture design for mapless navigation. The architecture is composed of several modules and each module generates behaviours. A novel method, inspired from a visual homing strategy, is adapted to a monocular vision-based system to overcome goal-based navigation problems. A neural network-based obstacle avoidance strategy is designed using a 2-D scanning laser. To evaluate the performance of the proposed architecture, the system has been tested using Microsoft Robotics Studio (MRS), which is a very powerful 3D simulation environment. In addition, real experiments to guide a Pioneer 3-DX mobile robot, equipped with a pan-tilt-zoom camera in a cluttered environment are presented. The analysis of the results allows us to validate the proposed behaviour-based navigation strategy.

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