Volume 4, No. 3, July 2015

General Information

  • ISSN: 2278-0149 (Online)
  • Abbreviated Title:  Int. J. Mech. Eng. Robot. Res.  
  • Editor-in-Chief: ​Prof Richard (Chunhui) Yang, Western Sydney University, Australia
  • Associate Editor: Prof. B.V. Appa Rao, Andhra University; Prof. Ian R. McAndrew, Capitol Technology University, USA
  • Managing Editor: Murali Krishna. B
  • DOI: 10.18178/ijmerr
  • Abstracting/Indexing: Scopus (since 2016), CNKI, Google Scholar, Crossref, etc.
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Autonomous Mobile Robot Self-Learning In Motion Planning Problem

Sergey V. Manko, Valery M. Lokhin, Sekou A. K. Diane, and Alexander S. Panin
MSTU MIREA, Moscow, Russia

Abstract—Increasing robotic systems autonomy is a major challenge in ensuring both their performance and ease of application in numerous areas of human activity. The present study attempts to combine several artificial intelligence methods to design self-learning control system for the task of mobile robot motion planning in a complex environment. We propose two main components of the control system: a dynamic path planner based on D* algorithm and terrain type prediction subsystem based on classification trees. Efficiency of both of the subsystems grows with time due to knowledge accumulation, leading the robot to a better maneuvering in the environment filled with obstacles.

Index Terms—autonomous mobile robot, motion planning, graph route planning, classification trees, machine learning

Cite: Sergey V. Manko, Valery M. Lokhin, Sekou A. K. Diane, and Alexander S. Panin, "Autonomous Mobile Robot Self-Learning In Motion Planning Problem," International Journal of Mechanical Engineering and Robotics Research, Vol.4, No. 3, pp. 238-241, July 2015. DOI: 10.18178/ijmerr.4.3.238-241