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基于圆弧模型的四足机器人步态规划 被引量：5

Gait Planning of Four-legged Robot Based on Arch-shaped Model
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摘要 根据A ibo机器狗前腿的圆弧型结构建立了机器人腿部的二维实体模型,并通过两个直角三角形解算A ibo机器狗的逆运动学.在此模型的基础上进行前腿在地面滚动行走的同心圆步态规划,并通过机器学习得到了平稳、快速的行走步态参数. According to the arch-shaped structure of Aibo robot＇s front leg, a two dimensional model of robot leg is built. Using two right-angled triangles, the inverse kinematics of Aibo is calculated. Based on this model, a concentric circle gait is planned to make Aibo walk with its front feet rolling on the ground, and the parameters of fast and stable walking are obtained with machine learning.
作者 张楫 赵明国 董浩
机构地区 清华大学自动化系
出处 《机器人》 EI CSCD 北大核心 2006年第5期536-539,共4页 Robot
基金 国家自然科学基金资助项目(60474204) 教育部博士点专项基金资助项目(20040003106)
关键词 步态规划 逆运动学 ROBOCUP gait planning inverse kinematics RoboCup
分类号 TP24 [自动化与计算机技术—检测技术与自动化装置]
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参考文献7

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同被引文献28

  • 1王新杰,李培根,陈学东,陈宏娟.四足步行机器人关节位姿和稳定性研究[J].中国机械工程,2005,16(17):1561-1566. 被引量：15
  • 2McGhee R B. Some finite state aspects of legged locomotion[J]. Mathematical Biosciences, 1968, 2(1/2): 67-84.
  • 3Pack D J, Kang H S. An omni directional gait control using a graph search method for a quadruped walking robot[C]//lEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 1995: 988-993.
  • 4Zhang R X. An evolutionary algorithm for trajectory based gait generation of biped robot[D]. Singapore: National University of Singapore, 1999: 5-8.
  • 5Buehler M, Battaglia R, Cocosco A, et al. Scout: A simple quadruped that walks, climbs, and runs[C]//IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 1998: 1707-1712.
  • 6Igarashi H, Kakikura M. Adaptive gait for a quadruped robot on 3D path planning[C]// IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 2003: 2049-2054.
  • 7Fujita M. AIBO: Toward the era of digital creatures[J]. International Journal of Robotics Research, 2001, 20(10): 781-794.
  • 8Hengst B, Ibbotson D, Pham S B, et al. Omnidirectional locomotion for quadruped robots[M]//Lecture Notes in Artificial Intelligence, vol.2377. Berlin, Germany: Springer-Verlag, 2002: 368-373.
  • 9Kohl N, Stone E Policy gradient reinforcement learning for fast quadrupedal locomotion[C]//IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 2004: 2619-2624.
  • 10Homby G S, Takamura S, Yamamoto T, et al. Autonomous evolution of dynamic gaits with two quadruped robots[J]. IEEE Transactions on Robotics, 2005, 21(3): 402-410.

引证文献5

二级引证文献48

机器人
2006年 第5期

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