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One-touch calibration of hum-noise-based touch sensor for unknown users utilizing models trained by different users 利用不同用户训练的模型对未知用户的基于嗡嗡声的触摸传感器进行一次触摸校准
IF 1.4 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2022-12-15 DOI: 10.1186/s40648-022-00238-4
Tzu-Hsuan Hsia, S. Okamoto, Yasuhiro Akiyama, Yoji Yamada
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引用次数: 1
Series admittance–impedance controller for more robust and stable extension of force control 串联导纳-阻抗控制器,使力控制的扩展更加稳健和稳定
IF 1.4 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2022-12-09 DOI: 10.1186/s40648-022-00237-5
Takuto Fujiki, K. Tahara
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引用次数: 2
A proposal for adaptive cruise control balancing followability and comfortability through reinforcement learning 一种利用强化学习平衡随动性和舒适性的自适应巡航控制方法
IF 1.4 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2022-12-08 DOI: 10.1186/s40648-022-00235-7
Nagayasu Maruyama, H. Mouri
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引用次数: 1
Development of a high-speed and low-torque loss two-way clutch 高速低转矩损失双向离合器的研制
IF 1.4 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2022-10-27 DOI: 10.1186/s40648-022-00236-6
Masaki Waragai, T. Takayama
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引用次数: 0
Clustering of distance sensors to transfer training data for relative position and orientation measurement devices 聚类的距离传感器传输训练数据的相对位置和方向测量设备
IF 1.4 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2022-09-30 DOI: 10.1186/s40648-022-00234-8
Sogo Amagai, Qiwei Ye, Yujiro Fukuoka, S. Warisawa, R. Fukui
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引用次数: 0
Automated harvesting by a dual-arm fruit harvesting robot 双臂水果收割机器人的自动收割
IF 1.4 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2022-09-22 DOI: 10.1186/s40648-022-00233-9
Takeshi Yoshida, Yuki Onishi, T. Kawahara, T. Fukao
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引用次数: 7
Optimization algorithm for feedback and feedforward policies towards robot control robust to sensing failures 面向机器人感知故障鲁棒控制的反馈与前馈策略优化算法
IF 1.4 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2022-07-14 DOI: 10.1186/s40648-022-00232-w
Taisuke Kobayashi, Kenta Yoshizawa
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引用次数: 3
Human-mimetic binaural ear design and sound source direction estimation for task realization of musculoskeletal humanoids 肌肉骨骼类人仿生双耳设计及声源方向估计
IF 1.4 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2022-06-17 DOI: 10.1186/s40648-022-00231-x
Omura, Yusuke, Kawaharazuka, Kento, Nagamatsu, Yuya, Koga, Yuya, Nishiura, Manabu, Toshimitsu, Yasunori, Asano, Yuki, Okada, Kei, Kawasaki, Koji, Inaba, Masayuki
Human-like environment recognition by musculoskeletal humanoids is important for task realization in real complex environments and for use as dummies for test subjects. Humans integrate various sensory information to perceive their surroundings, and hearing is particularly useful for recognizing objects out of view or out of touch. In this research, we aim to realize human-like auditory environmental recognition and task realization for musculoskeletal humanoids by equipping them with a human-like auditory processing system. Humans realize sound-based environmental recognition by estimating directions of the sound sources and detecting environmental sounds based on changes in the time and frequency domain of incoming sounds and the integration of auditory information in the central nervous system. We propose a human mimetic auditory information processing system, which consists of three components: the human mimetic binaural ear unit, which mimics human ear structure and characteristics, the sound source direction estimation system, and the environmental sound detection system, which mimics processing in the central nervous system. We apply it to Musashi, a human mimetic musculoskeletal humanoid, and have it perform tasks that require sound information outside of view in real noisy environments to confirm the usefulness of the proposed methods.
通过肌肉骨骼类人机器人识别类人环境对于在真实复杂环境中实现任务以及作为测试对象的假人具有重要意义。人类整合各种感官信息来感知周围环境,听觉在识别看不见或接触不到的物体时特别有用。在本研究中,我们旨在通过为肌肉骨骼类人机器人配备类人听觉处理系统,实现类人听觉环境识别和任务实现。人类通过估计声源的方向,根据传入声音的时频域变化和听觉信息在中枢神经系统的整合来检测环境声音,从而实现基于声音的环境识别。本文提出了一种拟人听觉信息处理系统,该系统由三个部分组成:拟人双耳单元(模仿人耳结构和特征)、声源方向估计系统和环境声检测系统(模仿中枢神经系统处理)。我们将其应用于武藏,一个模拟人类肌肉骨骼的类人机器人,并让它在真实嘈杂的环境中执行需要声音信息的任务,以确认所提出方法的实用性。
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引用次数: 0
An immersive micro-manipulation system using real-time 3D imaging microscope and 3D operation interface for high-speed and accurate micro-manipulation 采用实时三维成像显微镜和三维操作界面的沉浸式微操作系统,实现高速、精确的微操作
IF 1.4 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2022-06-11 DOI: 10.1186/s40648-022-00228-6
Kenta Yokoe, T. Aoyama, Toshiki Fujishiro, Masaru Takeuchi, Y. Hasegawa
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引用次数: 0
Fruit recognition method for a harvesting robot with RGB-D cameras RGB-D相机采摘机器人的水果识别方法
IF 1.4 Q3 INSTRUMENTS & INSTRUMENTATION Pub Date : 2022-05-28 DOI: 10.1186/s40648-022-00230-y
Yoshida, Takeshi, Kawahara, Takuya, Fukao, Takanori
In this study, we present a recognition method for a fruit-harvesting robot to automate the harvesting of pears and apples on joint V-shaped trellis. It is necessary to recognize the three-dimensional position of the harvesting target for harvesting by the fruit-harvesting robot to insert its end-effector. However, the RGB-D (red, green, blue and depth) camera on the harvesting robot has a problem in that the point cloud obtained in outdoor environments can be inaccurate. Therefore, in this study, we propose an effective method for the harvesting robot to recognize fruits using not only three-dimensional information obtained from the RGB-D camera but also two-dimensional images and information from the camera. Furthermore, we report a method for determining the ripeness of pears using the information on fruit detection. Through experiments, we confirmed that the proposed method satisfies the accuracy required for a harvesting robot to continuously harvest fruits.
在这项研究中,我们提出了一种水果收获机器人的识别方法,以实现梨和苹果在联合v型棚架上的自动收获。摘果机器人要插入末端执行器,必须对摘果目标的三维位置进行识别。然而,采收机器人上的RGB-D(红、绿、蓝、深)摄像头存在一个问题,即在室外环境下获得的点云可能不准确。因此,在本研究中,我们提出了一种有效的方法,使收获机器人既可以利用RGB-D相机获得的三维信息,又可以利用相机获得的二维图像和信息来识别水果。此外,我们报告了一种利用果实检测信息确定梨成熟度的方法。通过实验,我们证实了所提出的方法满足收获机器人连续收获水果的精度要求。
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引用次数: 13
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ROBOMECH Journal
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