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户外空中双机械手抓取设计和视觉伺服 Article
Pablo Ramon-Soria, Begoña C. Arrue, Anibal Ollero
《工程（英文）》 2020年 第6卷 第1期 页码 77-88 doi: 10.1016/j.eng.2019.11.003
本文介绍了一种配备有RGB-D摄像机的使用带有双机械手的无人飞行器（unmanned aerial vehicle, UAV）抓取已知物体的系统。本文主要从三个方面对这一任务进行了评价：目标检测与姿态估计、抓取设计、飞行中的抓取动作。人工神经网络（artificial neural network, ANN）首先被用来获得有关物体位置的线索。然后，使用物体的三维（three-dimensional, 3D）模型来估计空中机械手可实现良好抓取的排列清单。检测算法的结果（即对象的姿态）用于更新手臂朝向对象的轨迹。如果由于无人机的振荡而无法达到目标姿态，则算法将切换到下一个可行的抓取。本文介绍了总体方法，给出了每个模块的仿真实验结果和实际实验结果，并提供了视频演示结果。
关键词： 空中操纵，抓取设计，视觉伺服
基于注意力的高效机器人抓取检测网络 Research Article
秦晓飞1,胡文凯1,肖晨2,何常香2,裴颂文1,3,4,张学典1,3,4,5
《信息与电子工程前沿（英文）》 2023年 第24卷 第10期 页码 1430-1444 doi: 10.1631/FITEE.2200502
助老服务机器人系统设计及软件架构 Article
Norman Hendrich, Hannes Bistry, 张建伟
《工程（英文）》 2015年 第1卷 第1期 页码 27-35 doi: 10.15302/J-ENG-2015007
将智能人居辅助环境系统与服务机器人技术相结合，可以有效帮助老年人进行很多日常活动，有利于老年人获得更加良好的生活状态。本文概述了欧盟项目Robot-Era开发的智能人居辅助环境（AAL）系统，并重点阐述了系统中具有室内移动及物品操作能力的机器人的工程实现方法以及软件架构。该系统基于机器人操作系统（ROS）对大量先进的导航定位、环境感知以及操作控制算法进行集成，并通过实验对机器人的性能和实际应用效果进行验证。
跨行业标准测试方法进展：从制造机器人到可穿戴机器人 Review
Roger BOSTELMAN, Elena MESSINA, Sebti FOUFOU
《信息与电子工程前沿（英文）》 2017年 第18卷 第10期 页码 1447-1457 doi: 10.1631/FITEE.1601316
用于重建物理和虚拟抓取的可重构数据手套 Article
刘航欣, 张泽宇, 焦子元, Zhenliang Zhang, Minchen Li, 蒋陈凡夫, 朱毅鑫, Song-Chun Zhu
《工程（英文）》 2024年 第32卷 第1期 页码 203-220 doi: 10.1016/j.eng.2023.01.009
In this work, we present a reconfigurable data glove design to capture different modes of human hand–object interactions, which are critical in training embodied artificial intelligence (AI) agents for fine manipulation tasks. To achieve various downstream tasks with distinct features, our reconfigurable data glove operates in three modes sharing a unified backbone design that reconstructs hand gestures in real time. In the tactile-sensing mode, the glove system aggregates manipulation force via customized force sensors made from a soft and thin piezoresistive material; this design minimizes interference during complex hand movements. The virtual reality (VR) mode enables real-time interaction in a physically plausible fashion: A caging-based approach is devised to determine stable grasps by detecting collision events. Leveraging a state-of-the-art finite element method, the simulation mode collects data on fine-grained four-dimensional manipulation events comprising hand and object motions in three-dimensional space and how the object's physical properties (e.g., stress and energy) change in accordance with manipulation over time. Notably, the glove system presented here is the first to use high-fidelity simulation to investigate the unobservable physical and causal factors behind manipulation actions. In a series of experiments, we characterize our data glove in terms of individual sensors and the overall system. More specifically, we evaluate the system's three modes by ① recording hand gestures and associated forces, ② improving manipulation fluency in VR, and ③ producing realistic simulation effects of various tool uses, respectively. Based on these three modes, our reconfigurable data glove collects and reconstructs fine-grained human grasp data in both physical and virtual environments, thereby opening up new avenues for the learning of manipulation skills for embodied AI agents.
关键词： Data glove Tactile sensing Virtual reality Physics-based simulation
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