A Cyclic Inhibitory Central Pattern Generator Control Method Integrated with Mechanical Oscillators for Snake Robots
TANG Chaoquan1,2, WANG Minghui1, LI Bin1, MA Shugen1,3
1. The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Department of Technology, Ritsumeikan University, Kusatsu-Shi 525-8577, China
唐超权, 王明辉, 李斌, 马书根. 融合机械元的蛇形机器人循环抑制中枢模式发生器控制方法[J]. 机器人, 2013, 35(1): 123-128.DOI: 10.3724/SP.J.1218.2013.00123.
TANG Chaoquan, WANG Minghui, LI Bin, MA Shugen. A Cyclic Inhibitory Central Pattern Generator Control Method Integrated with Mechanical Oscillators for Snake Robots. ROBOT, 2013, 35(1): 123-128. DOI: 10.3724/SP.J.1218.2013.00123.
To solve the problem that there is no basis for choosing control signals and sensor information for central pattern generator (CPG) control of snake robots, a cyclic inhibitory CPG control method with mechanical oscillators is proposed. Firstly, the mechanical oscillators reformed from snake robot dynamical equations are introduced into the cyclic inhibitory CPG model. Secondly, an improved Matsuoka neuron is proposed so that the neuron and mechanical oscillator can be expressed in a uniform way. Thirdly, the relationship of parameters in the cyclic inhibitory CPG model with mechanical oscillators is illustrated, and the relationship expression of the control signal and sensor information for snake robots and the CPG states is given. Finally, the proposed method is verified with simulation, and the simulation results are analyzed. In this method, there are clear definitions for the control signals and sensor information of snake robots, and the computation complexity of CPG is decreased because neuron computation in CPG is replaced with physical structure of mechanical oscillators.
[1] Hirose S, Cave P, Goulden C. Biologically inspired robots: Snake-like locomotors and manipulators[M]. Oxford, UK: Oxford University Press, 1993.[2] Ijspeert A J. Central pattern generators for locomotion control in animals and robots: A review[J]. Neural Networks, 2008, 21(4): 642-653. [3] Taga G, Yamaguchi Y, Shimizu H. Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment[J]. Biological Cybernetics, 1991, 65(3): 147-159. [4] Kimura H, Fukuoka Y, Cohen A H. Adaptive dynamic walking of a quadruped robot on natural ground based on biological concepts[J]. International Journal of Robotics Research, 2007, 26(5): 475-490. [5] Arena P, Fortuna L, Frasca M, et al. An adaptive, self-organizing dynamical system for hierarchical control of bio-inspired locomotion[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(4): 1823-1837. [6] Lu Z L, Ma S G, Li B, et al. Serpentine locomotion of a snake-like robot controlled by cyclic inhibitory CPG model [C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ, USA: IEEE, 2005: 96-101.[7] Wu X D, Ma S G. Adaptive creeping locomotion of a CPG-controlled snake-like robot to environment change[J]. Autonomous Robots, 2010, 28(3): 282-294.[8] Sato M, Nakamura Y, Ishii S. Reinforcement learning for biped locomotion[C]//International Conference on Artificial Neural Networks. Berlin, Germany: Springer-Verlag, 2002: 777-782.[9] Crespi A, Ijspeert A J. Online optimization of swimming and crawling in an amphibious snake robot[J]. IEEE Transactions on Robotics, 2008, 24(1): 75-87. [10] Inoue K, Sumi T, Ma S G. CPG-based control of a simulated snake-like robot adaptable to changing ground friction[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ, USA: IEEE, 2007: 1957-1962.[11] Matsuoka K. Sustained oscillations generated by mutually inhibiting neurons with adaptation[J]. Biological Cybernetics, 1985, 52(6): 367-376.
