Recovery Motion Learning for Arm Mounted Mobile Crawler Robot in Drive System's Failure

Tasuku Ito, Hitoshi Kono, Yusuke Tamura, Atsushi Yamashita, Hajime Asama

Research output: Contribution to journalArticlepeer-review

Abstract

In the disaster area, an arm mounted crawler robot is leveraged for missions such as searching victims. However, the robot system has possibility of failure of drive system at the extreme environment. Moreover, the robot needs to keep moving to repair the mechanism, if the drive system becomes failure. In response to this problem, it is important to realize the recovery motion. However, designing of recovery motion is difficult because the recovery motion depends on the environments and configurations of the robot. This paper describes the learning methodology of the recovery motion in the single-arm mounted crawler robot, and we confirmed that the proposed system can learn the recovery motion in computer simulation.

Original languageEnglish
Pages (from-to)2329-2334
Number of pages6
JournalIFAC-PapersOnLine
Volume50
Issue number1
DOIs
Publication statusPublished - 2017 Jul
Externally publishedYes

Keywords

  • Crawler robot
  • Fault tolerance
  • Intelligent driver aids
  • Motion control
  • Normalized energy stability margin
  • Reinforcement learning
  • Teleoperation

ASJC Scopus subject areas

  • Control and Systems Engineering

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