• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Fakultet for informasjonsteknologi og elektroteknikk (IE)
  • Institutt for teknisk kybernetikk
  • View Item
  •   Home
  • Fakultet for informasjonsteknologi og elektroteknikk (IE)
  • Institutt for teknisk kybernetikk
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Autonomous driving of a small-scale electric truck model with dynamic wireless charging

Jon Eivind Stranden
Master thesis
Thumbnail
View/Open
no.ntnu:inspera:2455358.pdf (67.22Mb)
no.ntnu:inspera:2455358.zip (379.7Mb)
URI
http://hdl.handle.net/11250/2625670
Date
2019
Metadata
Show full item record
Collections
  • Institutt for teknisk kybernetikk [4103]
Abstract
 
 
Automated wireless power transfer can be seen as an enabling technology for autonomous

vehicles. At SINTEF Energy Research a 1/14 scale electric truck model [13] had been

used for demonstrating such technology. The truck was fitted with an induction system,

but was otherwise stock. In this master thesis a system for autonomous path following has

been developed and successfully implemented to demonstrate self-driving capabilities on

a path that includes an inductive charger. The purpose of the vehicle is to visualize the

concept of wireless charging for a self-driving truck, but on a smaller scale.

Three different methods has been tested and implemented.

1. A LiDAR-based approach that utilizes Hector SLAM for mapping and localization

together with a Pure Pursuit and Stanley Steering path tracking steering controller.

2. A machine-learning and camera-based approach that can navigate a track using deep

learning and behavior cloning/supervised learning. This was inspired by the convolutional

neural network used in Nvidia’s DAVE-2 self-driving car.

3. A classic computer vision approach for detecting lane curvature and using PID for

control.

ROS nodes that integrates the output from the SLAM- and deep learning-methods and

combines them with a path recorder and a path tracking algorithm has been made, together

with a graphical interface for monitoring vehicle states. Methods for estimating odometry

without wheel encoders, dynamic speed control and obstacle detection with start/stop

functionality has been implemented, along with a computer vision method for detecting

ArUco markers. A complete, self-driving ROS-based system has been created. An Nvidia

Jetson TX2 has been used as the main embedded computing unit. The system implements

Hector SLAM as the primary SLAM method, since it does not require odometry. The

Pure Pursuit path tracker was chosen as the preferred steering controller since it resulted

in smooth and stable tracking of the path. The neural network of the machine-learning

approach was able to steer the truck reliably and is implemented in the final version as

a separate mode. This works independently of the LiDAR for when the LiDAR is out

of range. The neural network has also been tested on the Udacity self-driving simulator

[4]. The computer vision approach was found to be too demanding for the embedded

hardware, and ended up not being used. The inductive charger system has successfully

been connected and integrated to the Jetson TX2 through a CAN-bus interface in order

to receive the battery state. The amount of charge received is dependent on the positioning

of the truck, and is not optimized for a system with manual path creation. A better

autonomous positioning system when driving across the charger is recommended, along

with a better and more precise LiDAR and vehicle platform.
 
Publisher
NTNU

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit