利用生物学灵感实现仿生双手机器人遥操作的双手动作捕捉。

IF 10.5 Q1 ENGINEERING, BIOMEDICAL Cyborg and bionic systems (Washington, D.C.) Pub Date : 2023-01-01 DOI:10.34133/cbsystems.0052
Qing Gao, Zhiwen Deng, Zhaojie Ju, Tianwei Zhang
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引用次数: 0

摘要

仿生双手机器人遥操作可以将人类双手的抓取和操纵技能转移到仿生双手机器人上,实现自然、灵活的操作。双手动作捕捉在远程操作中起着重要的作用。通过手部检测、定位和姿态估计,捕捉双手的运动信息,并将其映射到仿生双手机器人上,实现遥操作。然而,虽然动作捕捉技术近年来取得了很大的成就,但视觉双手动作捕捉仍然是一个很大的挑战。为此,本文提出了一种基于身体和手部生物灵感的双手检测方法和三维手部姿态估计方法,以实现方便、准确的单目双手动作捕捉和仿生双手机器人遥操作。首先,提出了一种基于身体结构约束的双手检测方法,该方法利用平行结构将手与身体的关系特征结合起来;其次,提出了一种考虑单幅RGB图像骨约束损失的三维手部姿态估计方法。然后,利用提出的手部检测和姿态估计方法,设计了一种仿生双手机器人遥操作方法。在公共手部数据集上的实验结果表明,该方法的手部检测和三维手部姿态估计的性能优于现有的方法。在仿生双手机器人遥操作平台上的实验结果表明了所提遥操作方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Dual-Hand Motion Capture by Using Biological Inspiration for Bionic Bimanual Robot Teleoperation.

Bionic bimanual robot teleoperation can transfer the grasping and manipulation skills of human dual hands to the bionic bimanual robots to realize natural and flexible manipulation. The motion capture of dual hands plays an important role in the teleoperation. The motion information of dual hands can be captured through the hand detection, localization, and pose estimation and mapped to the bionic bimanual robots to realize the teleoperation. However, although the motion capture technology has achieved great achievements in recent years, visual dual-hand motion capture is still a great challenge. So, this work proposed a dual-hand detection method and a 3-dimensional (3D) hand pose estimation method based on body and hand biological inspiration to achieve convenient and accurate monocular dual-hand motion capture and bionic bimanual robot teleoperation. First, a dual-hand detection method based on body structure constraints is proposed, which uses a parallel structure to combine hand and body relationship features. Second, a 3D hand pose estimation method with bone-constraint loss from single RGB images is proposed. Then, a bionic bimanual robot teleoperation method is designed by using the proposed hand detection and pose estimation methods. Experiment results on public hand datasets show that the performances of the proposed hand detection and 3D hand pose estimation outperform state-of-the-art methods. Experiment results on a bionic bimanual robot teleoperation platform shows the effectiveness of the proposed teleoperation method.

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CiteScore
7.70
自引率
0.00%
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审稿时长
21 weeks
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