<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>4340</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>12</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Navigating the Future: an approach of autonomous indoor vehicles</title>
    <abstract language="eng">In this project, we explored the ability of Reinforcement learning (RL) in driving an indoor car autonomously. RL has proven its good performance in solving challenging decision-making problems. Therefore, RL can be a promising solution for autonomous car to deal with complex driving scenarios. As hardware a model car eqipped with sensors and powerful computational unit has been used. We also utilized SLAM for environment mapping and a combination of lidar data and Wi-Fi technology for localization. The experiment showed that the model can perform very well in simulation. Although the model lacks the ability to drive the car as smoothly along a route, the car is still able to avoid obstacles and walls in an unknown real-world environment.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:863-opus-43401</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Julian Tilly</author>
    <author>Christopher Neeb</author>
    <author>Chandu Bhairapu</author>
    <author>Fatima Mohamed</author>
    <author>Indrasena Reddy Kachana</author>
    <author>Mahesh Saravanan</author>
    <author>Phuoc Nguyen Pham</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Autonomous vehicles</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>SLAM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Indoor localization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Reinforcement learning</value>
    </subject>
    <collection role="institutes" number="fiw">Fakultät Informatik und Wirtschaftsinformatik</collection>
    <thesisPublisher>Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/4340/Navigating_the_Future.pdf</file>
  </doc>
</export-example>
