TUHH Open Research
Help
  • Log In
    New user? Click here to register.Have you forgotten your password?
  • English
  • Deutsch
  • Communities & Collections
  • Publications
  • Research Data
  • People
  • Institutions
  • Projects
  • Statistics
  1. Home
  2. TUHH
  3. Publication References
  4. Deep Reinforcement Learning for Mobile Robot Navigation
 
Options

Deep Reinforcement Learning for Mobile Robot Navigation

Publikationstyp
Conference Paper
Date Issued
2019-07
Sprache
English
Author(s)
Gromniak, Martin  
Stenzel, Jonas  
Institut
Medizintechnische Systeme E-1  
TORE-URI
http://hdl.handle.net/11420/4754
Start Page
68
End Page
73
Article Number
8935944
Citation
Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019: 8935944 (2019-07)
Contribution to Conference
2019 4th Asia-Pacific Conference on Intelligent Robot Systems, ACIRS 2019  
Publisher DOI
10.1109/ACIRS.2019.8935944
Scopus ID
2-s2.0-85078564733
While navigation is arguable the most important aspect of mobile robotics, complex scenarios with dynamic environments or with teams of cooperative robots are still not satisfactory solved yet. Motivated by the recent successes in the reinforcement learning domain, the application of deep reinforcement learning to robot navigation was examined in this paper. In particular this required the development of a training procedure, a set of actions available to the robot, a suitable state representation and a reward function. The setup was evaluated using a simulated real-time environment. A reference setup, different goal-oriented exploration strategies and two different robot kinematics (holonomic, differential) were compared in the evaluation. In a challenging scenario with obstacles at changing locations in the environment the robot was able to reach the desired goal in 93% of the episodes.
Subjects
end-to-end-learning
mobile robot navigation
reinforcement learning
robot learning
TUHH
Weiterführende Links
  • Contact
  • Send Feedback
  • Cookie settings
  • Privacy policy
  • Impress
DSpace Software

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science
Design by effective webwork GmbH

  • Deutsche NationalbibliothekDeutsche Nationalbibliothek
  • ORCiD Member OrganizationORCiD Member Organization
  • DataCiteDataCite
  • Re3DataRe3Data
  • OpenDOAROpenDOAR
  • OpenAireOpenAire
  • BASE Bielefeld Academic Search EngineBASE Bielefeld Academic Search Engine
Feedback