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一种用于类人机器人敏捷方向盘操纵的摩擦驱动策略。

A Friction-Driven Strategy for Agile Steering Wheel Manipulation by Humanoid Robots.

作者信息

Cai Zhaoyang, Zhu Xin, Gergondet Pierre, Chen Xuechao, Yu Zhangguo

机构信息

School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.

CNRS-AIST Joint Robotics Laboratory, IRL, Tsukuba, Japan.

出版信息

Cyborg Bionic Syst. 2023 Nov 20;4:0064. doi: 10.34133/cbsystems.0064. eCollection 2023.

DOI:10.34133/cbsystems.0064
PMID:38435676
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10907019/
Abstract

Vehicle driving can substantially enhance the maneuverability of humanoid robots. Agile steering wheel manipulation requires rapid rotation in narrow spaces such as a cab, serving as the foundation for increasing driving speed, especially in an obstacle avoidance scenario. Generally, there are 3 human driving strategies, "Hand-to-Hand," "Hand-over-Hand," and "One-Hand." Based on the human driving motion data, we quantitatively analyze these strategies from 3 aspects, motion range of joint combination, motion region of the shoulder, and velocity of the manipulation. Then, a friction-driven manipulation strategy using one hand is proposed utilizing the similarity between a humanoid robot and a driver (human). It effectively addresses the requirements of both a small range of motion and rapid manipulation. To prevent the deformation of the steering wheel caused by excessive force, we construct an operating force model specifically for the steering wheel. This model accurately describes the relationship between the rotation resistance and the state of the steering wheel. In addition, we propose a quadratic programming (QP)-based control framework to servo the robot to track the end-effector position and target wrench output by this model. Finally, the effectiveness of this paper is evaluated through an obstacle avoidance scenario, achieving a maximum rotation velocity of 3.14 rad/s.

摘要

车辆驾驶可显著提高人形机器人的机动性。灵活的方向盘操纵需要在驾驶室等狭窄空间内快速旋转,这是提高驾驶速度的基础,尤其是在避障场景中。一般来说,有三种人工驾驶策略,即“双手交替”“双手交叉”和“单手”。基于人工驾驶运动数据,我们从关节组合的运动范围、肩部的运动区域和操纵速度三个方面对这些策略进行了定量分析。然后,利用人形机器人与驾驶员(人类)之间的相似性,提出了一种单手摩擦驱动操纵策略。它有效地满足了小运动范围和快速操纵的要求。为防止因用力过大导致方向盘变形,我们专门构建了一个针对方向盘的操作力模型。该模型准确描述了旋转阻力与方向盘状态之间的关系。此外,我们提出了一种基于二次规划(QP)的控制框架,以使机器人伺服跟踪该模型输出的末端执行器位置和目标扳手。最后,通过避障场景评估了本文方法的有效性,实现了3.14 rad/s的最大旋转速度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/2917131fde10/cbsystems.0064.fig.011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/7a2c7eac93fe/cbsystems.0064.fig.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/257dc06a62d2/cbsystems.0064.fig.003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/af83cc5df0ce/cbsystems.0064.fig.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/0a866a60f5b5/cbsystems.0064.fig.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/2917131fde10/cbsystems.0064.fig.011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/7a2c7eac93fe/cbsystems.0064.fig.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/2b18546348a9/cbsystems.0064.fig.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/257dc06a62d2/cbsystems.0064.fig.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/65ac67d15127/cbsystems.0064.fig.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/d3473e3e658d/cbsystems.0064.fig.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/315a74af0fa2/cbsystems.0064.fig.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/ab76db23c009/cbsystems.0064.fig.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/af83cc5df0ce/cbsystems.0064.fig.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/0a866a60f5b5/cbsystems.0064.fig.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1881/10907019/2917131fde10/cbsystems.0064.fig.011.jpg

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