Next Article in Journal
Preparation of Cu2O-Reduced Graphene Nanocomposite Modified Electrodes towards Ultrasensitive Dopamine Detection
Next Article in Special Issue
New Control Paradigms for Resources Saving: An Approach for Mobile Robots Navigation
Previous Article in Journal
Error Recovery in the Time-Triggered Paradigm with FTT-CAN
Previous Article in Special Issue
A Portable Dynamic Laser Speckle System for Sensing Long-Term Changes Caused by Treatments in Painting Conservation
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(1), 200; https://doi.org/10.3390/s18010200

Improving Odometric Accuracy for an Autonomous Electric Cart

Computer Science and System Department, Universidad de La Laguna, 38200 Santa Cruz de Tenerife, Spain
*
Author to whom correspondence should be addressed.
Received: 13 December 2017 / Revised: 9 January 2018 / Accepted: 10 January 2018 / Published: 12 January 2018
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2017)
View Full-Text   |   Download PDF [11639 KB, uploaded 12 January 2018]   |  

Abstract

In this paper, a study of the odometric system for the autonomous cart Verdino, which is an electric vehicle based on a golf cart, is presented. A mathematical model of the odometric system is derived from cart movement equations, and is used to compute the vehicle position and orientation. The inputs of the system are the odometry encoders, and the model uses the wheels diameter and distance between wheels as parameters. With this model, a least square minimization is made in order to get the nominal best parameters. This model is updated, including a real time wheel diameter measurement improving the accuracy of the results. A neural network model is used in order to learn the odometric model from data. Tests are made using this neural network in several configurations and the results are compared to the mathematical model, showing that the neural network can outperform the first proposed model. View Full-Text
Keywords: autonomous vehicles; odometry; neural networks; Robotics autonomous vehicles; odometry; neural networks; Robotics
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Toledo, J.; Piñeiro, J.D.; Arnay, R.; Acosta, D.; Acosta, L. Improving Odometric Accuracy for an Autonomous Electric Cart. Sensors 2018, 18, 200.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top