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arXiv 2107.12479cs.RO

Terrain-perception-free Quadrupedal Spinning Locomotion on Versatile Terrains: Modeling, Analysis, and Experimental Validation

Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)(深圳市人工智能与机器人研究院);Institute of Robotics and Intelligent Manufacturing (IRIM), The Chinese University of Hong Kong (CUHK)(香港中文大学机器人与智能制造研究院);George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology(佐治亚理工学院乔治·W·伍德拉夫机械工程学院);Beijing Institute for General Artificial Intelligence(北京通用人工智能研究院)

Hongwu Zhu, Dong Wang, Nathan Boyd, Ziyi Zhou, Lecheng Ruan, Aidong Zhang, Ning Ding, Ye Zhao, Jianwen Luo

英文摘要:

Dynamic quadrupedal locomotion over rough terrains reveals remarkable progress over the last few decades. Small-scale quadruped robots are adequately flexible and adaptable to traverse uneven terrains along sagittal direction, such as slopes and stairs. To accomplish autonomous locomotion navigation in complex environments, spinning is a fundamental yet indispensable functionality for legged robots. However, spinning behaviors of quadruped robots on uneven terrain often exhibit position drifts. Motivated by this problem, this study presents an algorithmic method to enable accurate spinning motions over uneven terrain and constrain the spinning radius of the Center of Mass (CoM) to be bounded within a small range to minimize the drift risks. A modified spherical foot kinematics representation is proposed to improve the foot kinematic model and rolling dynamics of the quadruped during locomotion. A CoM planner is proposed to generate stable spinning motion based on projected stability margins. Accurate motion tracking is accomplished with Linear Quadratic Regulator (LQR) to bound the position drift during the spinning movement. Experiments are conducted on a small-scale quadruped robot and the effectiveness of the proposed method is verified on versatile terrains including flat ground, stairs and slopes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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