TY - JOUR
T1 - Recovery system based on exploration-biased genetic algorithm for stuck rover in planetary exploration
AU - Uwano, Fumito
AU - Tajima, Yusuke
AU - Murata, Akinori
AU - Takadama, Keiki
N1 - Publisher Copyright:
© 2017, Fuji Technology Press. All rights reserved.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/10
Y1 - 2017/10
N2 - 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.
AB - 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.
KW - Adaptive learning
KW - Genetic algorithm
KW - Planetary exploration
KW - Rover
KW - Stabilizer
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U2 - 10.20965/jrm.2017.p0877
DO - 10.20965/jrm.2017.p0877
M3 - Article
AN - SCOPUS:85031906775
VL - 29
SP - 877
EP - 886
JO - Journal of Robotics and Mechatronics
JF - Journal of Robotics and Mechatronics
SN - 0915-3942
IS - 5
ER -