Local Path Planning Method of the Self-propelled Model Based on Reinforcement Learning in Complex Conditions

Conducting hydrodynamic and physical motion simulation tests using a large-scale self-propelled model under actual wave conditions is an important means for researching environmental adaptability of ships. During the navigation test of the self-propelled model, the complex environment including various port facilities, navigation facilities, and the ships nearby must be considered carefully, because in this dense environment the impact of sea waves and winds on the model is particularly significant. In order to improve the security of the self-propelled model, this paper introduces the Q learning based on reinforcement learning combined with chaotic ideas for the model’s collision avoidance, in order to improve the reliability of the local path planning. Simulation and sea test results show that this algorithm is a better solution for collision avoidance of the self navigation model under the interference of sea winds and waves with good adaptability.

• Record URL:
  • https://doi.org/10.1007/s11804-014-1265-7
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/16719433
• Supplemental Notes:
  • Copyright © 2014, Harbin Engineering University and Springer-Verlag Berlin Heidelberg.
• Authors:
  • Yang, Yi
  • Pang, Yongjie
  • Li, Hongwei
  • Zhang, Rubo
• Publication Date: 2014-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 333-339
• Serial:
  • Journal of Marine Science and Application
  • Volume: 13
  • Issue Number: 3
  • Publisher: Harbin Engineering University
  • ISSN: 1671-9433
  • EISSN: 1993-5048
  • Serial URL: http://link.springer.com/journal/11804

Subject/Index Terms

• TRT Terms: Algorithms; Crash avoidance systems; Navigation; Obstructions (Navigation); Ship simulators; Ships; Waves; Wind
• Subject Areas: Environment; Marine Transportation; Vehicles and Equipment; I15: Environment;

Filing Info

• Accession Number: 01545691
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 26 2014 4:01PM