Recovery system based on exploration-biased genetic algorithm for stuck rover in planetary exploration

Fumito Uwano, Yusuke Tajima, Akinori Murata, Keiki Takadama

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Contributing toward continuous planetary surface exploration by a rover (i.e., a space probe), this paper proposes (1) an adaptive learning mechanism as the software system, based on an exploration-biased genetic algorithm (EGA), which intends to explore several behaviors, and (2) a recovery system as the hardware system, which helps a rover exit stuck areas, a kind of immobilized situation, by testing the explored behaviors. We develop a rover-type space probe, which has a stabilizer with two movable joints like an arm, and learns how to use them by employing EGA. To evaluate the effectiveness of the recovery system based on the EGA, the following two field experiments are conducted with the proposed rover: (i) a small field test, including a stuck area created artificially in a park; and (ii) a large field test, including several stuck areas in Black Rock Desert, USA, as an analog experiment for planetary exploration. The experimental results reveal the following implications: (1) the recovery system based on the EGA enables our rover to exit stuck areas by an appropriate sequence of motions of the two movable joints; and (2) the success rate of getting out of stuck areas is 95% during planetary exploration.

Original languageEnglish
Pages (from-to)877-886
Number of pages10
JournalJournal of Robotics and Mechatronics
Issue number5
Publication statusPublished - Oct 2017
Externally publishedYes


  • Adaptive learning
  • Genetic algorithm
  • Planetary exploration
  • Rover
  • Stabilizer

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering


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