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通用地形上无需地形感知的四足旋转运动:建模、分析与实验验证

Terrain-Perception-Free Quadrupedal Spinning Locomotion on Versatile Terrains: Modeling, Analysis, and Experimental Validation.

作者信息

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

机构信息

Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen, China.

Institute of Robotics and Intelligent Manufacturing (IRIM), The Chinese University of Hong Kong (CUHK), Shenzhen, China.

出版信息

Front Robot AI. 2021 Oct 26;8:724138. doi: 10.3389/frobt.2021.724138. eCollection 2021.

DOI:10.3389/frobt.2021.724138
PMID:34765648
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8576540/
Abstract

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 the 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 a stable spinning motion based on projected stability margins. Accurate motion tracking is accomplished with linear quadratic regulator (LQR) to bind 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.

摘要

在过去几十年中,四足机器人在崎岖地形上的动态运动取得了显著进展。小型四足机器人具有足够的灵活性和适应性,能够沿矢状方向穿越不平坦地形,如斜坡和楼梯。为了在复杂环境中实现自主运动导航,旋转是有腿机器人一项基本且不可或缺的功能。然而,四足机器人在不平坦地形上的旋转行为往往会出现位置漂移。受此问题启发,本研究提出一种算法方法,以实现四足机器人在不平坦地形上的精确旋转运动,并将质心(CoM)的旋转半径限制在较小范围内,以最大限度地降低漂移风险。提出一种改进的球形足部运动学表示方法,以改进四足机器人在运动过程中的足部运动学模型和滚动动力学。提出一种质心规划器,基于预测的稳定性裕度生成稳定的旋转运动。通过线性二次调节器(LQR)实现精确的运动跟踪,以抑制旋转运动过程中的位置漂移。在小型四足机器人上进行了实验,并在包括平地、楼梯和斜坡在内的多种地形上验证了所提方法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/43e2784b75fa/frobt-08-724138-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/b3b41ac2c306/frobt-08-724138-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/06a8857c89bf/frobt-08-724138-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/4ee4cb8ab10f/frobt-08-724138-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/ae9f11e29690/frobt-08-724138-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/2dfcf8079988/frobt-08-724138-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/e2501b32632e/frobt-08-724138-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/23d34a2605e9/frobt-08-724138-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/6f51a3db5a28/frobt-08-724138-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/43e2784b75fa/frobt-08-724138-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/b3b41ac2c306/frobt-08-724138-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/c4c549857d47/frobt-08-724138-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/06a8857c89bf/frobt-08-724138-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/4ee4cb8ab10f/frobt-08-724138-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/ae9f11e29690/frobt-08-724138-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/2dfcf8079988/frobt-08-724138-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/e2501b32632e/frobt-08-724138-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/23d34a2605e9/frobt-08-724138-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/6f51a3db5a28/frobt-08-724138-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69a1/8576540/43e2784b75fa/frobt-08-724138-g010.jpg

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本文引用的文献

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Multi-expert learning of adaptive legged locomotion.多专家学习自适应腿部运动。
Sci Robot. 2020 Dec 9;5(49). doi: 10.1126/scirobotics.abb2174.
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Biorobotics: using robots to emulate and investigate agile locomotion.生物机器人学:利用机器人模拟和研究敏捷运动。
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