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Wednesday, August 5, 2020 | History

2 edition of neural-network approach to high-precision docking of autonomous vehicles. found in the catalog.

neural-network approach to high-precision docking of autonomous vehicles.

Joseph Wong

neural-network approach to high-precision docking of autonomous vehicles.

by Joseph Wong

  • 158 Want to read
  • 35 Currently reading

Published .
Written in English


About the Edition

The objective of this Thesis is to develop a neural-network-based guidance methodology for high-precision short-range localization of autonomous vehicles (i.e., docking). The novelty of the overall system is its applicability to cases that do not allow for the direct proximity measurement of the vehicle"s pose.Herein, the line-of-sight based indirect proximity sensory feedback is used by the Neural-Network (NN) based guidance methodology for path-planning during the final stage of vehicle"s motion (i.e., docking). The corrective motion commands generated by the NN model are used to reduce the systematic motion errors of the vehicle accumulated after a long-range of motions in an iterative manner, until the vehicle achieves its desired pose within random noise limits. The overall vehicle-docking methodology developed provides effective guidance that is independent of the sensing-system"s calibration model. Comprehensive simulation and experimental studies have verified the proposed guidance methodology for high-precision vehicle docking.

The Physical Object
Pagination111 leaves.
Number of Pages111
ID Numbers
Open LibraryOL19215170M
ISBN 109780494161722

The navigation and localization of autonomous underwater vehicles (AUVs) in seawater are of the utmost importance for scientific research, petroleum engineering, search and rescue, and military missions concerning the special environment of seawater. However, there is still no general method for AUVs navigation and localization, especially in the featureless seabed. The reported approaches to. work instead employs a recurring neural network to model the steering dynamics of an autonomous vehicle. The resulting model is then integrated into a Nonlinear Model Predictive Control scheme to generate feedforward steering commands for embedded control. The proposed approach is compared to traditional rst-principles steering modeling through.

The path generated approximates any road shape with high precision, while satisfying the constraint of vehicle kinematic. The RBF is a kind of local approximation neural network with the advantage of fast learning speed; therefore, it can react fast to the environment and meet the real-time requirement of autonomous driving. tonomous vehicle re-uses technological blocks from ADAS, but contrary to theses partial automated systems on which the conductor has the decisional power, the autonomous vehicle requires the mas-sive introduction to safety methods, at the same title than nuclear technologies or aerospace. This self-driving car makes dream as it arouses fear.

  The car performed similarly running both the learned and physics-based systems, even though the neural network lacked explicit information about road friction. To drive in these adverse conditions, Stanford’s autonomous vehicles incorporate both in-the moment evaluations and physics-backed data from previous drives. Neural-network-based docking of autonomous vehicles. J Wong, G Nejat, RG Fenton, B Benhabib. IEEE Intelligent Transportation Systems Conference, , A neural-network approach to high-precision docking of autonomous vehicles. J Wong. The system can't perform the operation now. Try again later.


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Neural-network approach to high-precision docking of autonomous vehicles by Joseph Wong Download PDF EPUB FB2

In this paper, a Neural-Network- (NN) based guidance methodology is proposed for the high-precision docking of autonomous vehicles/platforms.

The novelty of the overall online motion-planning methodology is its applicability to cases that do not allow for the direct proximity measurement of the vehicle's pose (position and orientation).Cited by: 2.

A neural-network neural-network approach to high-precision docking of autonomous vehicles. book to high-precision docking of autonomous vehicles/platforms a Neural-Network- (NN) based guidance methodology is proposed for the high-precision docking of autonomous.

In this paper, a neural-network-based guidance methodology that utilizes line-of-sight based task-space sensory feedback is proposed for the localization of autonomous robotic vehicles. The novelty of the overall system is its applicability to cases that do not allow for the direct proximity measurement of the vehicle's pose (position and orientation).

In this paper, a neural-network-based guidance methodology that utilizes line-of-sight based task-space sensory feedback is proposed for the localization of autonomous robotic vehicles. In this paper, we present an autonomous docking system for electric vehicles recharging based on an embarked infrared camera performing infrared beacons detection installed in the infrastructure.

A visual servoing system coupled with an automatic controller allows the vehicle to dock accurately to the recharging booth in a street parking by: DOI: / Corpus ID: An efficient neural network approach to tracking control of an autonomous surface vehicle with unknown dynamics @article{PanAnEN, title={An efficient neural network approach to tracking control of an autonomous surface vehicle with unknown dynamics}, author={Chang-Zhong Pan and Xu-Zhi Lai and Simon X.

Yang and Min Wu}. 1. Introduction. Recently, commercial and military use of autonomous underwater vehicles (AUV) has rapidly increased [,, ].Docking algorithms, comprising navigation and guidance controllers, are used to guide the AUV safely to the dock [10, 20].However, no matter how good the guidance algorithm or controller is, sea currents and sensor errors can cause the AUV to land at a bad.

Automated vehicles navigate through their environment by first planning and subsequently following a safe trajectory. To prove safer than human beings, they must ultimately perform these tasks as well or better than human drivers across a broad range of conditions and in critical situations.

We show that a feedforward-feedback control structure incorporating a simple physics. Guidance map. In Sans-Muntadas et al.

() a spiral path was proposed for reaching a docking station and at the same time keep the transmitter or landmark mounted on the docking station for navigation purposes within the field of view (FOV) of the vehicle. In this paper, we propose an alternative solution where we use a guidance map instead of planning a specific path.

Electric vehicles are progressively introduced in urban areas, because of their ability to reduce air pollution, fuel consumption and noise nuisance.

Nowadays, some big cities are launching the first electric car-sharing projects to clear traffic jams and enhance urban mobility, as an alternative to the classic public transportation systems. However, there are still some problems to be solved.

In this paper, a neural-network-based guidance methodology is proposed for the docking of autonomous vehicles. The novelty of the overall system is its applicability to cases that do not allow for the direct proximity measurement of the vehicle's pose (position and orientation).

A Neural-Network Approach for High-Precision Docking of Autonomous Vehicles Journal of Robotica. Localization of Autonomous Robotic Vehicles Using a Neural-Network Approach IEEE Proceedings of International Conference on Intelligent Robots and : Counterparty Credit Risk. An Application of Neural Networks to an Autonomous Car Driver K.

Albelihi 1and D. Vrajitoru 1Computer and Information Sciences Department, Indiana University South Bend, South Bend, IN, USA Abstract—In this paper, we present a car driving system called “Gazelle” for a simulated racing competition.

For this. The use of Neural Networks (NN) is necessary to bring the behavior of Intelligent Autonomous Vehicles (IAV) near the human one in recognition, learning, decision-making, and action. First, current navigation approaches based on NN are discussed. Indeed, these current approaches remedy insufficiencies of classical approaches related to real-time,autonomy, and intelligence.

Second, a neural. Its ability to learn from the past could prove particularly powerful, given the abundance of autonomous car data researchers are producing in the process of developing these vehicles. Physics and learning with a neural network.

Control systems for autonomous cars need access to information about the available road-tire friction. Deep-Neural-Network-driven Autonomous Cars ICSE ’18, May June 3,Gothenburg, Sweden Figure 2: A simple autonomous car DNN that takes inputs from camera, light detection and ranging sensor (LiDAR), and IR (in-frared) sensor, and outputs steering angle, braking decision, and acceleration decision.

The DNN shown here essentially models the. The paper presents an original approach for visual identification of road direction of an autonomous vehicle using an improved Radial Basis Function (RBF) neural network.

We present the results of designing, software implementation, training, and testing of our RBF model for automatic road direction detection as a function of the input image. PhD Project: Integrated underwater navigation and mapping based on imaging and hydro-acoustic sensors. Main supervisor: Kristin Y.

Pettersen Co-supervisor: Edmund F. Brekke PhD Project Focus: Autonomous surface or subsea launch and recovery of AUVs (Docking).Specially fousing on single node Hydro-acoustic Navigation. Background: I have a bachelor in electrical engineering from the. A Neural Network for Automatic Vehicles Guidance.

Alessandro Ghidotti Piovan Universit a di Bologna, Italy [email protected] Abstract. The purpose of this work involves the application and the evaluation of a Reinforcement Learning (RL) based approach to address the problem of controlling the steering of a vehicle. The car is.

Nejat and B. Benhabib, "Docking of Autonomous Vehicles: A Comparison of Model-Independent Guidance Methods," Virtual Int.

Conference on Intelligent Production Machines and Systems, pp. 1. ALVINN [6] (autonomous land vehicle in a neural network) uses a separate three-layer Feed-Forward Neural Network structured at ( input, 29 hidden, 45 output) neurons to achieve very stable navigation at 1m/s.

ALVINN’s approach to driving as a classification approach was very similar to .Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles: /ch Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide.

They have.InDean Pomerleau published a research report on ALVINN (Autonomous Land Vehicle in a Neural Network), an artificial neural network designed for autonomous land vehicle .