ROBOT. Research and Implementation of Automatic Gait Evolution for 4-Legged Robot

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31 1 2009 1 ROBOT Vol.31, No.1 Jan., 2009 1002-0446(2009)-01-0072-05 201804 RoboCup PC 40 60 min ERS-7 ERS-7 27 cm/s 43 cm/s TP242.6 A Research and Implementation of Automatic Gait Evolution for 4-Legged Robot XU Tao CHEN Qi-jun (Department of Control Science and Engineering, Tongji University, Shanghai 201804, China) Abstract: By adopting evolution algorithm and autonomous-vision-based fitness evaluation approaches, the on-line automatic gait evolution of 4-legged robot in a RoboCup soccer field is realized. We incorporate interpolation method as the crossover method, use a PC base station to conduct algorithm calculation and flow control, and adopt some time-cutting strategies. The evolutionary learning is implemented with high continuity and expansibility, and the whole learning process can be completed within 40 60 minutes. In-field gait re-learning of the ERS-7 4-legged robot is realized, and the adaptability of walking control is improved. At last, the walking speed of ERS-7 4-legged robots is increased from 27 cm/s to 43 cm/s with the proposed algorithm. Keywords: 4-legged robot; gait learning; evolution algorithm; walking control 1 Introduction [1] [2] Powell [3] [4] UNSW UTAustin RoboCup [5,6] 60875057 2008-04-10

31 1 73 [7,8] 2 Aibo 4-legged robot Aibo and its locomotion model Aibo ERS-7 1 [9] 3 3 1 1 2 20 3 31 1 30 double 1 4 6 10 0 1 1 4 1 2 1 1 31 2 Fig.2 Locus of modified elliptical gait (the origin of coordinate is the geometrical center of the robot body) 1 3 Aibo ERS-7 Fig.1 The 4-legged robot Aibo ERS-7 with 3DOF for each leg 2 3 Walking gait evolution approaches

74 2009 1 UNSW UTAustin 3.1 [70 mm,170 mm] Aibo [10] 3.2 r os r mut g i+1 = g i + θ rand a (1) g i g i+1 rand 0 1 θ a ( f,m) w 0 < w < 1 g i+1 = f i w + m i (1 w) + ( f i m i ) δ (2) g i+1 f i m i i δ 0 1 r c 3.3 RoboCup [11] RoboCup [12,13] 3 RoboCup Fig.3 A 4-legged robot evolves its walking gait in a RoboCup official field 3 4

31 1 75 40 cm/s 3.4 3.5 PC 4 ERS7 IEEE802.11b LAN PC TCP/IP PC 20 min 40 60 min PC PC n P(g) g P(g) = {x 1,x 2,,x n } Ft r os Begin g = 1 P(g) Ft(x i ) 1 While (running) Begin for each x i in P(g) if Ft(x i ) = 1 then Ft(x i ) Ft P(g) P(g) (1 r os ) n P(g + 1) g = g + 1 PC PC t S V m = S/t Fig.4 4 Evolution algorithm for 4-legged robot gait optimization 4 Experimental results and analysis 43 cm/s 5 1

76 2009 1 5 5 24 30 RoboCup 1 Tab.1 Gait evolution control parameters in the experiment N 10 r os 0.5 r mut 0.05 a 0.2 r c 0.6 5 Conclusion (a) (b) 5 Fig.5 Experiment result of 4-legged robot gait evolution 24 60 300 120 Aibo ERS-7 43 cm/s 27 cm/s 59.3% 40 60 min TJArk 2007 RoboCup 6 Acknowledgment TJArk TJArk 81

31 1 81 3D 3 References [1] Vukobratović M, Borovac B. Zero-moment point Thirty five years of its life[j]. International Journal of Humanoid Robotics, 2004, 1(1): 157 173. [2] Honda Motor Co., Ltd. Smoother and more stable walking [EB/OL]. http://world.honda.com/asimo/technology/walking. html, 2008. [3] McGeer T. Passive dynamic walking[j]. The International Journal of Robotics Research, 1990, 19(2): 62 82. [4] Collins S, Ruina A, Tedrake R, et al. Efficient bipedal robots based on passive-dynamic walkers[j]. Science, 2005, 307 (5712): 1082 1085. [5] Anderson S O, Wisse M, Atkeson C G, et al. Powered bipeds based on passive dynamic principles[a]. Proceedings of the IEEE/RAS International Conference on Humanoid Robots [C]. Piscataway, NJ, USA: IEEE, 2005. 110 116. [6] Wisse M, Feliksdal G, van Frankenhuyzen J, et al. Passivebased walking robot Denise, a simple, efficient, and lightweight biped[m]. IEEE Robotics & Automation Magazine, 2007, 14(2): 52 62. [7] Garcia M, Chatterjee A, Ruina A, et al. The simplest walking model: Stability, complexity, and scaling[j]. Transactions of the ASME, Journal of Biomechanical Engineering, 1998, 120(2): 281 288. 1983 1972 76 References [1] Hengst B, Ibbotson D, Pham S B, et al. The UNSW RoboCup 2000 Sony legged league team[a]. RoboCup 2000: Robot Soccer World Cup IV[C]. Berlin, Germany: Springer, 2001. 64 75. [2] Mitchell T M. Machine Learning[M]. USA: McGraw-Hill, 1997. [3] Powell M J D. A method for nonlinear constraints in minimization problems[a]. Optimization[M]. London, UK: Academic Press, 1969. 283 298. [4] Baird L, Moore A W. Gradient descent for general reinforcement learning[j]. Advances in Neural Information Processing Systems, 1999, 11: 968 974. [5] Kim M S, Uther W. Automatic gait optimization for quadruped robots[a]. Proceedings of the Australasian Conference on Robotics and Automation[C]. Brisbane, Australia: Australian Robotics and Automation Association, 2003. [6] Kohl N, Stone P. Policy gradient reinforcement learning for fast quadrupedal locomotion[a]. Proceedings of the IEEE International Conference on Robotics and Automation[C]. Piscataway NJ, USA: IEEE, 2004. 2619 2624. [7] Zhang J Q, Chen Q J. Lamarckian evolution and Baldwin effect in neural networks[j]. DCDIS A Supplement, Advances in Neural Networks, 2007, 14(S1): 470 473. [8] Bäck T. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms[M]. New York, USA: Oxford University Press, 1996. [9] Sony Corporation. Model information for ERS-7[EB/OL]. http:// www.robots.newcastle.edu.au/ QuinlanThesis/ ModelInformation 7 E.pdf, 2004. [10] Duffert U. Quadruped walking modeling and optimization of robot movements[eb/ol]. http://uwe-dueffert.de/publication/ dueffert04 diploma.pdf, 2004-07-22. [11] Gutmann J S. Markov-Kalman localization for mobile robots [A]. Proceedings of the 16th International Conference on Pattern Recognition[C]. Piscataway, NJ, USA: IEEE, 2002. 601 604. [12] Quinlan M J, Nicklin S P, Hong K, et al. Nubot final report 2005[EB/OL]. http://robots.newcastle.edu.au/publications/ NUbotFinalReport2005.pdf, 2006-02-02. [13] Chen Q J, Xie H L, Woo P Y. Vision-based fast objects recognition and distances calculation of robots[a]. 31st Annual Conference of IEEE Industrial Electronics Society[C]. Piscataway, NJ, USA: IEEE, 2005. 363 368. 1985