1 edition of Neural Network Perception for Mobile Robot Guidance found in the catalog.
Vision-based mobile robot guidance has proved difficult for classical machine vision methods because of the diversity and real-time constraints inherent in the task. This book describes a connectionist system called ALVINN (Autonomous Land Vehicle In a Neural Network) that overcomes these difficulties. ALVINN learns to guide mobile robots using the back-propagation training algorithm. Because of its ability to learn from example, ALVINN can adapt to new situations and therefore cope with the diversity of the autonomous navigation task. But real world problems like vision-based mobile robot guidance present a different set of challenges for the connectionist paradigm. Among them are: how to develop a general representation from a limited amount of real training data; how to understand the internal representations developed by artificial neural networks; how to estimate the reliability of individual networks; how to combine multiple networks trained for different situations into a single system; how to combine connectionist perception with symbolic reasoning. Neural Network Perception for Mobile Robot Guidance presents novel solutions to each of these problems. Using these techniques, the ALVINN system can learn to control an autonomous van in under 5 minutes by watching a person drive. Once trained, individual ALVINN networks can drive in a variety of circumstances, including single-lane paved and unpaved roads, and multi-lane lined and unlined roads, at speeds of up to 55 miles per hour. The techniques also are shown to generalize to the task of controlling the precise foot placement of a walking robot.
|Statement||by Dean A. Pomerleau|
|Series||The Springer International Series in Engineering and Computer Science -- 239, International series in engineering and computer science -- 239.|
|LC Classifications||TJ210.2-211.495, TJ163.12|
|The Physical Object|
|Format||[electronic resource] /|
|Pagination||1 online resource (xv, 191 pages).|
|Number of Pages||191|
|ISBN 10||1461364000, 1461531926|
|ISBN 10||9781461364009, 9781461531920|
sensing inputs and neural network perception of terrain texture. The system employs off-road driving heuristics to facilitate avoidance of hazardous vehicle configurations and excessive wheel slippage. In each case, our system is designed to produce safe speed recommendations associated with the current perception of the safety status of the rover. What is there? Perception of Indoor Images. A Perception System for Mobile Robot Localisation. Visual Guidance for Autonomous Robots: A Case Study. Intelligent Visual Sensing System for Autonomous Applications. Depth Estimation by Adaptive Regulation of Camera Parameters. A Neural Network for Optic Flow Computation through Subgraph Isomorphism.
RAM-Based Neural Network for Collision Avoidance in a Mobile Robot. Qiang Yao, Daryl Beetner, Donald C. Wunsch. I1. and Bjom Osterloh* Department. of. Electrical & Computer Engineering, University of Missouri-Rolla, Rolla, MO, * Institut fuer Datentechnik und Kommunikationsnetze, Technische Universitaet Braunschweig, Braunschweig Cited by: 9. Vision based mobile robot guidance has proven difficult for classical machine vision methods because of the diversity and real time constraints inherent in the task. This thesis describes a connectionist system called ALVINN (Autonomous Land Vehicle In a Neural Network) that overcomes these by:
Semantic Scholar extracted view of "Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto, Adaptive Computation and Machine Learning series, MIT Press (Bradford Book), Cambridge, Mass., , xviii + pp, ISBN , (hardback, £)" by Alex M. Andrew. the robot guidance problem into subtasks, having a separate module to perform each subtask. The local navigation module (l.n.m.) is a fuzzy neural net, based on the ASAFES2 algorithm . This net, having been trained by reinforcement learning, controls the robot, avoiding obstacles and trying to head to a target. This idea has been presented.
Dean Pomerleau's trainable road tracker, ALVINN, is arguably the world's most famous neural net application. It currently holds the world's record for distance traveled by an autonomous robot without interruption: miles along a highway, in traffic, at speedsofup to 55 miles per hour.
Neural Network Perception for Mobile Robot Guidance presents novel solutions to each of these problems. Using these techniques, the ALVINN system can learn to control an autonomous van in under 5 minutes by watching a person drive. Neural Network Perception for Mobile Robot Guidance - Ebook written by Dean A.
Pomerleau. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Neural Network Perception for Mobile Robot Guidance.
Get this from a library. Neural network perception for mobile robot guidance. [Dean A Pomerleau] -- Vision based mobile robot guidance has proven difficult for classical machine vision methods because of the diversity and real time constraints inherent in the task.
This book describes a. Neural Network Perception for Mobile Robot Guidance (The Springer International Series in Engineering and Computer Science) [Pomerleau, Dean A.] on *FREE* shipping on qualifying offers.
Neural Network Perception for Mobile Robot Guidance (The Springer International Series in Engineering and Computer Science)Format: Paperback. A neural network (NN) performs the system model identification that will be used to design the appropriate intelligent mobile robot controller.
The usage of NN for controlling a mobile robot is justified from the following reasons: the operational conditions considered raises complex nonholonomic mobile robot kinematics and NN has universal Cited by: 7.
A reply to Towell's book review of Neural Network Perception for Mobile Robot Guidance Dean A. Pomerleau 1 Machine Learning vol pages – () Cite this articleAuthor: Dean A.
Pomerleau. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.
Keywords: Mobile Robot, Neural Network, Ultrasound Range Finder, Path Planning, Navigation 1. Introduction. neural network perception for mobile robot guidance are a good way to achieve details about operating certainproducts.
Many products that you buy can be obtained using instruction manuals. network perception for mobile robot guidance is packed with valuable instructions, information and.
Neural Network Perception for Mobile Robot Guidance Book Dean Pomerleau's trainable road tracker, ALVINN, is arguably the world's most famous neural net application. In this paper we introduce a neural networks-based approach for planning collision-free paths among known stationary obstacles in structured environment for a robot Janglová, D.
/ Neural Networks in Mobile Robot Motion, pp.Inernational Journal of Advanced Robotic Systems, Volume 1 Number 1 (), ISSN 16 with translational. Neural Network Perception for Mobile Robot Guidance presents novel solutions to each of these problems.
Using these techniques, the ALVINN system can learn to. Neural networks from the ground up [Book Review] We propose an adaptive control and an adaptive neural network control (composed of two RBF neural components and one adaptive component) for Author: Michael J. Lutz. This paper presents a visual/motor behavior learning approach, based on neural networks.
We propose Behavior Chain Model (BCM) in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are : Lejla Banjanovic-Mehmedovic, Dzenisan Golic, Fahrudin Mehmedovic, Jasna Havic.
A Learning Approach to Neural Control (E Blanzieri et al.) The Point of View of Perception: Visual Perception and Conceptual Spaces (E Ardizzone et al.) What is There. Perception of Indoor Images (G Adorni et al.) A Perception System for Mobile Robot Localisation (R Cassinis et al.) Visual Guidance for Autonomous Robots: A Case Study (G Adorni.
Neural Network Controller for a Mobile Robot. Report. Browse more videos. Playing next. Neural Net Mobile Robot Controller - Attempt 1. Santos Daniel. ELSEVIER Robotics and Autonomous Systems 16 () Robotics and Autonomous Systems Artificial neural network for mobile robot topological localization Janusz Racz 1,2, Artur Dubrawski 3 Institute of Fundamental Technological Research, Polish Academy of Sciences, 21 Swietokrzyska Str., Warsaw, Poland Abstract This paper presents a neural network based approach to a mobile Cited by: This video shows a novel unsupervised learning algorithm building and training an artificial neural network which is controller a mobile robot simulator.
Key Words: Mobile robots, Intelligent Motion Control, Neural Networks Control, Real Time Control, Artificial Vision, Road Following. INTRODUCTION It is possible to guide a mobile by means of the information obtained from the road, when certain brightness requirements as well as road appearance ones are complying by: 5.
 Kai-Hui Chi, Min-Fan Ricky Lee () “Obstacle Avoidance in Mobile Robot using neural network”, /11/ IEEE  Beom, H.
and H. Cho (). “A Sensor-based Obstacle Avoidance Controller For A Mobile Robot Using Fuzzy Logic And Neural Network.” Intelligent Robots and Systems,Proceedings. mobile robot that can learn the dynamic model of the robot was proposed , where the learning algorithm of the controller is computationally expensive, causing a slow convergence.
A reinforcement learning method uses a neural network with Q-learning to navigate an industrial vehicle in unknown environments by avoiding collisions .Cited by: 7.NEURAL NETWORK IMPLEMENTATION CONTROL MOBILE ROBOT S.
Parameshwara1, Manjunath A. C.2, Vishnu Bhat Yalakki2, Madhu S.2, Amaresh Hiremath2 1 Assistant Professor, Department of Electronics and Communication Engineering, The National Institute of Engineering, Mysuru, IndiaThis paper presents development and control of a disc-typed one-wheel mobile robot, called GYROBO.
Several models of the one-wheel mobile robot are designed, developed, and controlled. The current version of GYROBO is successfully balanced and controlled to follow the straight line. GYROBO has three actuators to balance and move. Two actuators are used for balancing control by virtue of gyro Cited by: