Recovery System Based on Exploration-Biased Genetic Algorithm for Stuck Rover in Planetary Exploration

Fumito Uwano; Yusuke Tajima; Akinori Murata; Keiki Takadama

doi:10.20965/jrm.2017.p0877

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JRM Vol.29 No.5 pp. 877-886

doi: 10.20965/jrm.2017.p0877

(2017)

Paper:

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Recovery System Based on Exploration-Biased Genetic Algorithm for Stuck Rover in Planetary Exploration

Fumito Uwano, Yusuke Tajima, Akinori Murata, and Keiki Takadama

The University of Electro-Communications
1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan

Received:

April 27, 2017

Accepted:

August 28, 2017

Published:

October 20, 2017

Keywords:

adaptive learning, genetic algorithm, rover, stabilizer, planetary exploration

Abstract

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.

Proposed rover

Cite this article as:

F. Uwano, Y. Tajima, A. Murata, and K. Takadama, “Recovery System Based on Exploration-Biased Genetic Algorithm for Stuck Rover in Planetary Exploration,” J. Robot. Mechatron., Vol.29 No.5, pp. 877-886, 2017.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] E. Dupuis, M. Picard, T. Haltigin, T. Lamarche, S. Rocheleau, and D. Gingras, “Results from the CSA’ s 2015 Mars Analogue Mission in the Desert of Utah,” Proc. of the 2016 Int. Symposium on Artificial Intelligence, Robotics and Automation in Space, 2016.

[2] [2] K. Yoshida, “Achievements in space robotics,” IEEE Robotics Automation Magazine, Vol.16, No.4, pp. 20-28, December 2009.

[3] [3] Y. Yuguchi, W. Tesshin, K. Maruya, K. Nagaoka, and K. Yoshida, “Microgravity Experiment of Rock Climbing Locomotion for Exploration Robot on Minor Body,” Proc. of the 13th Int. Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 2016), #S-3a-2, 2016.

[4] [4] F. Cordes and A. Babu, “SherpaTT: A Versatile Hybrid Wheeled-Leg Rover,” Proc. of the 13th Int. Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 2016), 2016.

[5] [5] K. Iizuka, T. Sasaki, H. Hama, A. Nishitani, T. Kubota, and I. Nakatani, “Development of a small, lightweight rover with elastic wheels for lunar exploration,” J. of Robotics and Mechatronics, Vol.24, No.6, pp. 1031-1039, 2012.

[6] [6] K. Sakamoto, M. Otsuki, T. Kubota, and Y. Morino, “RFT-based Analysis of Mobility Performance of Hopping Rover on Soft Soil for Planetary Exploration,” Proc. of the 13th Int. Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 2016), 2016.

[7] [7] J. Tao, Z. Deng, M. Hu, J. Liu, and Z. Bi, “A small wheeled robotic rover for planetary exploration,” 1st Int. Symposium on Systems and Control in Aerospace and Astronautics 2006 (ISSCAA 2006), 2006.

[8] [8] D. E. Goldberg, “Genetic algorithms in search, optimization, and machine learning, 1989,” Reading: Addison-Wesley, 1989.

[9] [9] K. Endo, T. Maeno, and H. Kitano, “Co-evolution of morphology and walking pattern of biped humanoid robot using evolutionary computation – evolutionary designing method and its evaluation,” Proc. 2003 IEEE/RSJ Int. Conf on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), Vol.1, pp. 340-345, Oct. 2003.

[10] [10] J.-Y. Lee and J.-J. Lee, “Optimal walking trajectory generation for a biped robot using multi-objective evolutionary algorithm,” 2004 5th Asian Control Conf (IEEE Cat. No.04EX904), Vol.1, pp. 357-364, July 2004.

[11] [11] A. W. Salatian and Y. F. Zheng, “Gait synthesis for a biped robot climbing sloping surfaces using neural networks. I. Static learning,” Proc. 1992 IEEE Int. Conf on Robotics and Automation, pp. 2601-2606, Vol.3, May 1992.

[12] [12] A. W. Salatian and Y. F. Zheng, “Gait synthesis for a biped robot climbing sloping surfaces using neural networks, II: Dynamic learning,” Proc. 1992 IEEE Int. Conf on Robotics and Automation, pp. 2607-2611, Vol.3, May 1992.

[13] [13] S. Nakasuka, “Students’ Challenges towards New Frontier-Enlarging Activities of UNISEC and Japanese Universities,” Trans. of The Japan Society For Aeronautical And Space Sciences, Space Technology Japan, Vol.7, No.ists26, To_1_1–To_1_6, 2009.

[14] [14] K. Iagnemma, S. Kang, H. Shibly, and S. Dubowsky, “Online terrain parameter estimation for wheeled mobile robots with application to planetary rovers,” IEEE Trans. on Robotics, Vol.20, No.5, pp. 921-927, 2004.

[15] [15] O. Kazumi and T. Takeshi, “Navigation for a Behavior-Based Autonomous Mobile Robot,” J. of Robotics and Mechatronics, Vol.10, No.5, pp. 407-412, 1998.

[16] [16] A. Yasunori, T. Yoshio, S. Shintaro, F. Toshio, A. Fumihito, and S. Masaru, “Error Recovery of Autonomous Mobile Robot With Global Matching,” J. of Robotics and Mechatronics, Vol.12, No.3, pp. 235-241, 2000.