Advanced Materials Technologies
·
2019
·Vol. 4
·期 11
·1900602
·
IF 5.395
Guozhen Li,Rong Zhu
Multisensory tactile systems play an important role in enhancing robot intelligence. A competent robotic tactile system needs to be simple in structure and easily operated, especially with multiple sensations, and have good coordinate ability like human skin. A novel multisensory tactile system for humanoid robotic hands is proposed, allowing the hand to identify objects by grasping and manipulating them. Robotic multifunction sensors based on skin-inspired thermosensation and structured with micro platinum ribbons partially covered with piezo-thermic silver-nanoparticle-doped porous polydimethylsiloxane membrane simultaneously and independently detect contact pressure, local ambient temperature, and thermal conductivity and temperature of an object. Multiple tactile information which is obtained by the robotic sensors is fused comprehensively based on neural network classification to identify diverse objects in uncertain and dynamic gripping with an object recognition accuracy of 95%. This multisensory system enables robotic hand to better interact with its environment, enhances robotic intelligence, and makes various complex tasks feasible for robots, such as sorting material or rescuing from fire or other disasters.
研究领域:
机器人学
·
生物启发工程
·
人工智能
✓ 已验证
产品关联
DOI
高影响因子
Polymers
·
2022
·Vol. 14
·期 19
·3930
·
IF 4.967
Xiao Cheng,Fan Zhang,Wentao Dong
Electronic skin with human-like sensory capabilities has been widely applied to artificial intelligence, biomedical engineering, and the prosthetic hand for expanding the sensing ability of robots. Robotic electronic skin (RES) based on conductive hydrogel is developed to collect strain and pressure data for improving the grasping capability of the robot finger. RES is fabricated and assembled by the soft functional materials through a sol–gel process for guaranteeing the overall softness. The strain sensor based on piezoresistive hydrogel (gauge factor ~9.98) is integrated onto the back surface of the robot finger to collect the bending angle of the robot finger. The capacitive pressure sensor based on a hydrogel electrode (sensitivity: 0.105 kPa−1below 3.61 kPa, and 0.0327 kPa−1in the range from 4.12 to 15 kPa.) is adhered onto the fingertip to collect the pressure data when touching the objects. A robot-finger-compatible RES with strain and pressure sensing function is designed for finger gesture detection and grasping manipulation. The negative force feedback control framework is built to improve grasping manipulation of the robot finger with RES, which would provide a self-adaptive control method to determine whether the objects are grasped successfully or not. Robot fingers integrated with soft sensors would promote the development of sensing and grasping abilities of the robot finger and interaction with human beings.
研究领域:
材料科学
·
机器人技术
·
生物医学工程
✓ 已验证
DOI
高影响因子
ADVANCED MATERIALS
·
2023
·Vol. 36
·期 7
·2310555
·
IF 29.4
Wenjie Wang,Yingfei Wang,Li Xiang,Long Chen,Lilei Yu,Anlian Pan,Jie Tan,Quan Yuan
Pain sensation is a crucial aspect of perception in the body. Force-activated nociceptors encode electrochemical signals and yield multilevel information of pain, thus enabling smart feedback. Inspired by the natural template, multi-dimensional mechano-sensing materials provide promising approaches for biomimetic nociceptors in intelligent terminals. However, the reliance on non-centrosymmetric crystals has narrowed the range of these materials. Here centrosymmetric crystal Cr 3+ -doped zinc gallogermanate (ZGGO:Cr) with multi-dimensional mechano-sensing is reported, eliminating the limitation of crystal structure. Under forces, ZGGO:Cr generates electrical signals imitating those of neuronal systems, and produces luminescence for spatial mapping of mechanical stimuli, suggesting a path toward bionic pain perception. On that basis, a wireless biomimetic nociceptor system is developed and a smart pain reflex in a robotic hand and robot-assisted biopsy surgery of rat and dog is achieved.
研究领域:
生物医学工程
·
材料科学
·
神经科学
✓ 已验证
DOI
高影响因子
Materials Today Physics
·
2024
·
IF 10
Yangfan Liu,Changliu He,Jiahao Fang,Jian Zhou,Kedong Shang,Xiaobo Pu,Linmao Qian,Tingting Yang
Developing simple device configurations to satisfy independent thermal and force sensing is critical for improving the recognition and manipulation capabilities of robotic hands on objects, but it remains a challenge. In this paper, through the composition adjustment of polyvinyl alcohol, KOH, nano-silica, glycerin, and the microporous structure design, the prepared ionic hydrogel sensor has promising comprehensive performance. It shows thermal sensitivity (−0.47 % °C −1 , 0–20 °C; −0.24 % °C −1 , 20–50 °C), pressure sensitivity (1 kPa −1 , 0–300 kPa), pressure detection limit of 1 Pa, and mechanical durability (1000 compression fatigue cycles at 100 kPa). More importantly, the pressure-sensitive and thermal-sensitive properties hardly interfere with each other due to the ionic hydrogel network's synergy of excellent water retention, anti-freezing properties and heat insulation ability. The multifunctional tactile sensor is integrated into a robot hand to obtain force and thermal information of the measured object, and combined with machine learning algorithms to realize object recognition. The proposed sensing scheme has been applied to 6 kinds of fruit and vegetable classification tasks in a cold chain logistics scenario, showing a classification accuracy of 95 %.
研究领域:
材料科学
·
机器人学
·
机器学习
✓ 已验证
产品关联
DOI
高影响因子
SENSORS AND ACTUATORS A-PHYSICAL
·
2021
·
IF 3.407
Zhiqiang Gao,Bing Ren,Zhaozhou Fang,Huiqiang Kang,Jing Han,Jie Li
The application of flexible wearable sensors in the grasping process of robot hand can recognize the contour, soft and hard, material, surface temperature and other information of the grasping object, which can effectively improve the intelligent level of the robot. In this work, a method of object contour recognition is proposed by combining the flexible PVDF polymer piezoelectric sensor and high conductivity hydrogel piezoresistive sensor aiming at the problem of profile recognition for objects of the same or similar material. The response of flexible piezoresistive sensor to the static strain is used to sense the angular displacement of robot fingers, and then the shape and size of the object is recognized indirectly. At the same time, the flexible piezoelectric sensor is used as the fingertip tactile sensor to reflect the surface morphology of the object through the dynamic strain information when touching the object. In the whole process of grasping the object, the dual-mode strain information is fully used to realize the recognition of the shape, size and surface morphology of the object. Combining these information, the accurate recognition of the object contour can be further realized. In the experiments, six objects with different shape and four objects with different surface morphology are recognized to verify the feasibility of piezoresistive sensors and piezoelectric sensors respectively. In a comprehensive experiment, eight objects made of the same rubber material with different shape, size and surface morphology are recognized, and the average recognition rate is about 84%, which shows good classification advantages for the objects with similar shape, size and material.
研究领域:
智能机器人
·
传感器技术
·
材料科学
✓ 已验证
产品关联
DOI
高影响因子
ACS Applied Materials & Interfaces
·
2021
·
IF 9.229
Wentuo Yang,Mengying Xie,Xiaoshuang Zhang,Xueyou Sun,Cheng Zhou,Ye Chang,Hainan Zhang,Xuexin Duan
Robotic hands with tactile perception can perform more advanced and safer operations, such as material recognition. Nanowires with high sensitivity, fast response, and low power consumption are suitable for multifunctional flexible tactile sensors to provide the tactile perception of robotic hands. In this work, we designed a multifunctional soft robotic finger with a built-in nanoscale temperature–pressure tactile sensor for material recognition. The flexible multifunctional tactile sensor integrates a nanowire-based temperature sensor and a conductive sponge pressure sensor to measure the temperature change rate and contact pressure simultaneously. The developed nanoscale temperature and conductive sponge pressure sensor can reach a high sensitivity of 1.196%/°C and 13.29%/kPa, respectively. With this multifunctional tactile sensor, the soft finger can quickly recognize four metals within three contact pressure ranges and 13 materials within a high contact pressure range. By combining tactile information and artificial neural networks, the soft finger can recognize the materials precisely with a high recognition accuracy of 92.7 and 95.9%, respectively. This work proves the application potential of the multifunctional soft robot finger in material recognition.
研究领域:
机器人技术
·
传感器技术
·
人工智能
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Science Robotics
·
2020
·Vol. 5
·期 49
·
IF 18.684
Guozhen Li,Shiqiang Liu,Liangqi Wang,Rong Zhu
Robot hands with tactile perception can improve the safety of object manipulation and also improve the accuracy of object identification. Here, we report the integration of quadruple tactile sensors onto a robot hand to enable precise object recognition through grasping. Our quadruple tactile sensor consists of a skin-inspired multilayer microstructure. It works as thermoreceptor with the ability to perceive thermal conductivity of a material, measure contact pressure, as well as sense object temperature and environment temperature simultaneously and independently. By combining tactile sensing information and machine learning, our smart hand has the capability to precisely recognize different shapes, sizes, and materials in a diverse set of objects. We further apply our smart hand to the task of garbage sorting and demonstrate a classification accuracy of 94% in recognizing seven types of garbage.
研究领域:
机器人学
·
传感器
·
人工智能
·
材料科学
·
机械工程
✓ 已验证
DOI
高影响因子
Nano Energy
·
2022
·
IF 19.069
Hongsen Niu,Hao Li,Yang Li,Wenjing Yue,Song Gao,Xiao Wei,Guozhen Shen
Traditional electronic skin (e-skin) with single micro-nano structures has a limited capacity to optimize the comprehensive sensing properties, which greatly hinders the intellectualization process. Herein, cocklebur-inspired bionic e-skin consisting of the “branch-seed-spininess” three-dimensional hierarchical structure, namely the P(VDF-TrFE) fiber-TiO 2 pillar-TiO 2 thorn structure (FPTS), is proposed. Benefiting from the hierarchical characteristics of the FPTS, the e-skin exhibits a high sensitivity of 0.163 kPa −1 (<20 kPa), wide pressure sensing range of 0–200 kPa, and ultrafast response/relaxation time of < 5.6 ms. In light of these salient features , versatile intelligent perception applications are explored. First, robot fingers are equipped with the FPTS e-skins and realize the traditional Chinese medicine pulse diagnosis of the radial artery in different positions and pressing degrees. Second, a flexible perception array with 100 FPTS e-skin units enables the spatial pressure mapping of flat/curved surfaces and the perception of different Braille characters. Most importantly, by making full use of the FPTS e-skin with a five-layer deep neural network , an intelligent gesture perception system is developed for accurate recognition (average accuracy: 98.33%) of sign language and precise control of the robot hand, which is of great significance for the development of intelligent sign language translation in artificial intelligence.
研究领域:
材料科学
·
机器人技术
·
人工智能
·
生物医学工程
✓ 已验证
产品关联
DOI
高影响因子
IEEE SENSORS JOURNAL
·
2024
·Vol. 24
·期 8
·12178-12185
·
IF 4.3
Yuyan Zhang,Tian Sun,Youchi Jia,Zhixin Ren,Zixiang Zhou,Yintang Wen
The soft hand allows for flexible and safe object grasping by utilizing tactile sensitivity to gather information about the object, thereby minimizing potential damage to its surface. However, the integration design of the drive and sensor faces significant challenges due to variations in driving mode and substrate material properties. This research presents a soft hand design based on a liquid crystal elastomer (LCE) integrated with a capacitive pressure sensor in the finger. The finger-bending part of the soft hand is composed of an LCE driven by a low-pressure (6 V) electric heating film, and the finger sensor component consists of a flexible capacitive pressure sensor with a grid-like structure of electrodes and a PU sponge medium. Among them, the electric heating film is prepared from polyimide (PI) with imprinted gate electrodes, and the flexible capacitive sensor is prepared from an electrode composed of LCEs with an imprinted grid structure and filled with multiwalled carbon nanotubes (MWCNTs), with a porous PU sponge as the medium. The flexible capacitive pressure sensor has a sensitivity of up to $0.06875\,\,\text {kPa}^{-{1}}$ in the low-pressure range. This method of integrating the sensor on an LCE film achieves pressure sensing and demonstrates the potential of LCE films for gripping objects and sensing recognition applications.
研究领域:
生物医学工程
·
柔性电子
·
软体机器人
·
智能材料
·
触觉传感
·
人机交互
✓ 已验证
DOI
高影响因子
IEEE SENSORS JOURNAL
·
2025
·
IF 4.5
Wentao Dong,Chang Chen,Mengyuan Xiong,Lin Yang,Giancarlo Fortino
Robot hand requires more sensing data to perform grasping tasks safely. It is difficult to ensure the stability and adaptability of the robot hand during the grasping process with single sensing data, which hinders the advanced capabilities and real time performance of robot hand. The real time control system based on flexible dual-modal sensors (FDMSs) and embedded multilayer perceptron (MLP) model is built to enhance performance of robot hand in precision operations and complex tasks. FDMSs are applied to simultaneously detect the proximity distance and contact pressure of the target objects for providing richer tactile information during the grasping process of the robot hand. Lightweight neural network models are deployed on STM32F4 microprocessor for processing the modal sensing data from FDMSs. Robot hand with FDMSs could grasp different objects successfully with proximity distance and contact pressure signal feedback control strategies, which improves the operational precision and response time of robot hand during the grasping process. Comparative experiments indicate that the embedded AI system with sensory feedback could adjust grasping force to avoid damaging objects. Robot hand with FDMSs and embedded AI demonstrates good adaptability of grasping various objects by adjusting the grasping strategy based on multi-sensing data, ensuring safe and effective task completion.
研究领域:
机器人学
·
传感器
·
人工智能
·
嵌入式系统
·
电子皮肤
·
触觉感知
·
控制系统
✓ 已验证
产品关联
DOI
高影响因子
Materials Today Bio
·
2022
·
IF 10.761
Rui Li,Ya Zhao,Hongli Fan,Mingqian Chen,Wenjun Hu,Qiang Zhang,Meilin Jin,Gang L. Liu,Liping Huang
Accurate determination of the concentration and viability of the viral vaccine vectors is urgently needed for preventing the spread of the viral infections, but also supporting the development and assessment of recombinant virus-vectored vaccines. Herein, we describe a nanoplasmonic biosensor with nanoscale robot hand structure (Nano RHB) for the rapid, direct, and specific capture and quantification of adenovirus particles. The nanorobot allows simple operation in practical applications, such as real-time monitoring of vaccine quantity and quality, and evaluation of vaccine viability. Modification of the Nano RHB with branched gold nanostructures allow rapid and efficient assessment of human adenovirus viability, with ultrahigh detection sensitivity of only 100 copies/mL through one-step sandwich method. Nano RHB detection results were consistent with those from the gold standard median tissue culture infectious dose and real-time polymerase chain reaction assays. Additionally, the Nano RHB platform showed high detection specificity for different types of viral vectors and pseudoviruses. Altogether, these results demonstrate that the Nano RHB platform is a promising tool for efficient and ultrasensitive assessment of vaccines and gene delivery vectors.
研究领域:
生物技术
·
纳米技术
·
病毒学
·
分析化学
·
药学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Materials Today
·
2024
·
IF 21.1
Jize Liu,Wei Zhao,Zhichao Ma,Hongwei Zhao,Luquan Ren
Flexible and biocompatible self-driven sensors capable of multimodal perception are pivotal for the rapid development of wearable electronic devices. Currently, few studies have integrated the sensing of both frictional force and vertical pressure into a single, self-driven flexible sensor. Herein, we developed an acrylate (AA)-polyglutamic acid (PGA) hydrogel material that exhibits mechanical properties similar to those of skin. By integrating a triboelectric nanogenerator (TENG) into the AA-PGA hydrogel matrix, we constructed a dual-mode flexible sensor capable of utilizing biomechanical energy for multidirectional force sensing. This sensor features high sensitivity, high linearity, fast response, and excellent stability. A prototype was proposed for a robotic hand e-skin monitoring and analysis system to demonstrate the performance of the AA-PGA hydrogel sensor. As a self-driven sensor, the AA-PGA hydrogel sensor can perform real-time monitoring of physiological signals, such as wrist pulse detection and voice recognition. Moreover, it was continuously showcased in a series of personalized monitoring scenarios, including handwriting, step counting, and respiratory monitoring, further substantiating its outstanding sensing capabilities. Given these advantages, the developed AA-PGA hydrogel sensor holds great promise for applications in wearable sensors, physiological monitoring, and human–machine interfaces.
研究领域:
材料科学
·
纳米技术
·
生物医学工程
·
可穿戴技术
·
机器人技术
·
传感器
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）