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2025 Volume 47 Issue 8
Article Contents

YANG Yongfan, DING Junsheng, GUI Yingang, et al. Preparation and Grasping Application of Self-Powered Tactile Sensor Based on Hybrid Working Mode[J]. Journal of Southwest University Natural Science Edition, 2025, 47(8): 205-215. doi: 10.13718/j.cnki.xdzk.2025.08.017
Citation: YANG Yongfan, DING Junsheng, GUI Yingang, et al. Preparation and Grasping Application of Self-Powered Tactile Sensor Based on Hybrid Working Mode[J]. Journal of Southwest University Natural Science Edition, 2025, 47(8): 205-215. doi: 10.13718/j.cnki.xdzk.2025.08.017

Preparation and Grasping Application of Self-Powered Tactile Sensor Based on Hybrid Working Mode

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  • Corresponding author: GUI Yingang ; 
  • Received Date: 08/11/2024
    Available Online: 20/08/2025
  • MSC: TH113.1

  • Triboelectric nanogenerators (TENG) have been widely applied in the field of robot tactile perception. However, the high-impedance output signals pose challenges for portable acquisition and practical applications. At the same time, there is a lack of robot grasping strategies that utilize the working characteristics of triboelectric sensors. To address these challenges, a self-powered tactile sensor that integrated both contact-separation and lateral sliding modes was proposed, enabling tactile perception of contact and sliding through distinct characteristic signals generated by these modes. Furthermore, a signal conditioning circuit that employed a charge-to-voltage conversion method was designed and fabricated, effectively converting high-impedance weak charge signals into low-impedance voltage signals, thus facilitating portable TENG signal detection. Based on this setup, a robot grasping strategy that enabled real-time interaction between the control system and the tactile sensor was proposed, allowing for the determination of the minimum clamping force during the grasping process. This approach enhances the stability of the robot's grasp and reduces potential damage to the object being handled. These advancements contribute to the broader application of triboelectric tactile sensors in robotic grasping systems.

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  • [1] GRAU A, INDRI M, BELLO L L, et al. Robots in Industry: The Past, Present, and Future of a Growing Collaboration with Humans[J]. IEEE Industrial Electronics Magazine, 2021, 15(1): 50-61.

    Google Scholar

    [2] MCLEAY F, OSBURG V S, YOGANATHAN V, et al. Replaced by a Robot: Service Implications in the Age of the Machine[J]. Journal of Service Research, 2021, 24(1): 104-121.

    Google Scholar

    [3] ROSHANIANFARD A, NOGUCHI N. Pumpkin Harvesting Robotic End-Effector[J]. Computers and Electronics in Agriculture, 2020, 174: 105503.

    Google Scholar

    [4] GAO J, ZHANG F, ZHANG J X, et al. Development and Evaluation of a Pneumatic Finger-Like End-Effector for Cherry Tomato Harvesting Robot in Greenhouse[J]. Computers and Electronics in Agriculture, 2022, 197: 106879.

    Google Scholar

    [5] TAWK C, SARIYILDIZ E, ALICI G. Force Control of a 3D Printed Soft Gripper with Built-in Pneumatic Touch Sensing Chambers[J]. Soft Robotics, 2022, 9(5): 970-980.

    Google Scholar

    [6] XIE Z X, DOMEL A G, AN N, et al. Octopus Arm-Inspired Tapered Soft Actuators with Suckers for Improved Grasping[J]. Soft Robotics, 2020, 7(5): 639-648.

    Google Scholar

    [7] MANTI M, HASSAN T, PASSETTI G, et al. A Bioinspired Soft Robotic Gripper for Adaptable and Effective Grasping[J]. Soft Robotics, 2015, 2(3): 107-116.

    Google Scholar

    [8] GAO S, DAI Y N, NATHAN A. Tactile and Vision Perception for Intelligent Humanoids[J]. Advanced Intelligent Systems, 2022, 4(2): 2100074.

    Google Scholar

    [9] TANG W, LIU R, SHI Y B, et al. From Finger Friction to Brain Activation: Tactile Perception of the Roughness of Gratings[J]. Journal of Advanced Research, 2020, 21: 129-139.

    Google Scholar

    [10] ZHANG B H, XIE Y X, ZHOU J, et al. State-of-the-Art Robotic Grippers, Grasping and Control Strategies, as Well as Their Applications in Agricultural Robots: A Review[J]. Computers and Electronics in Agriculture, 2020, 177: 105694.

    Google Scholar

    [11] NARENDIRAN A, GEORGE B. Capacitive Tactile Sensor with Slip Detection Capabilities for Robotic Applications[C]//2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, May 11-14, 2015, Pisa, Italy. IEEE, 2015: 464-469.

    Google Scholar

    [12] JAVAID S, HIRANO H, TANAKA S, et al. Surface Covering Structure and Active Sensing with MEMS-CMOS Integrated 3-Axis Tactile Sensors for Object Slip Detection and Texture Recognition[C]//2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers), June 20-24, 2021, Orlando, FL, USA. IEEE, 2021: 222-225.

    Google Scholar

    [13] LI X, MING Z, WANG K Y. Design of Multifunctional Touch and Slip Sensor System Based on PVDF Piezoelectric Film[J]. Journal of Physics: Conference Series, 2021, 1838(1): 012024.

    Google Scholar

    [14] FENG J H, JIANG Q. Slip and Roughness Detection of Robotic Fingertip Based on FBG[J]. Sensors and Actuators A: Physical, 2019, 287: 143-149.

    Google Scholar

    [15] SHAN B C, LIU C X, CHEN R H, et al. A Self-Powered Sensor for Detecting Slip State and Pressure of Underwater Actuators Based on Triboelectric Nanogenerator[J]. Materials Today Nano, 2023, 24: 100391.

    Google Scholar

    [16] WANG X D, LIANG J M, XIAO Y X, et al. A Flexible Slip Sensor Using Triboelectric Nanogenerator Approach[J]. Journal of Physics Conference, 2018, 986: 012009.

    Google Scholar

    [17] SOTER G, CONN A, HAUSER H, et al. Bodily Aware Soft Robots: Integration of Proprioceptive and Exteroceptive Sensors[C]//2018 IEEE International Conference on Robotics and Automation (ICRA), May 21-25, 2018, Brisbane, QLD, Australia. IEEE, 2018: 2448-2453.

    Google Scholar

    [18] YAN G, SCHMITZ A, TOM O T P, et al. Detection of Slip from Vision and Touch[C]//2022 International Conference on Robotics and Automation (ICRA), May 23-27, 2022, Philadelphia, PA, USA. IEEE, 2022: 3537-3543.

    Google Scholar

    [19] QU J T, MAO B J, LI Z K, et al. Recent Progress in Advanced Tactile Sensing Technologies for Soft Grippers[J]. Advanced Functional Materials, 2023, 33(41): 2306249.

    Google Scholar

    [20] ZHU J X, ZHU M L, SHI Q F, et al. Progress in TENG Technology-A Journey from Energy Harvesting to Nanoenergy and Nanosystem[J]. EcoMat, 2020, 2(4): e12058.

    Google Scholar

    [21] ZHANG R Y, OLIN H. Material Choices for Triboelectric Nanogenerators: A Critical Review[J]. EcoMat, 2020, 2(4): e12062.

    Google Scholar

    [22] CHENG T H, SHAO J J, WANG Z L. Triboelectric Nanogenerators[J]. Nature Reviews Methods Primers, 2023, 3: 39.

    Google Scholar

    [23] FAN F R, TIAN Z Q, WANG Z L. Flexible Triboelectric Generator[J]. Nano Energy, 2012, 1(2): 328-334.

    Google Scholar

    [24] CHEN J, WANG Z L. Reviving Vibration Energy Harvesting and Self-Powered Sensing by a Triboelectric Nanogenerator[J]. Joule, 2017, 1(3): 480-521.

    Google Scholar

    [25] WU Z Y, CHENG T H, WANG Z L. Self-Powered Sensors and Systems Based on Nanogenerators[J]. Sensors, 2020, 20(10): 2925.

    Google Scholar

    [26] HOU W C, TANG X L, FANG L, et al. Self-Driven Real-Time Angle Vector Sensor as Security Dialer Based on Bi-Directional Backstop Triboelectric Nanogenerator[J]. Nano Energy, 2022, 99: 107430.

    Google Scholar

    [27] JING Q S, XIE Y N, ZHU G, et al. Self-Powered Thin-Film Motion Vector Sensor[J]. Nature Communications, 2015, 6: 8031.

    Google Scholar

    [28] DENG J Z, WU Z Y, HUO X Q, et al. Triboelectric Based Smart Ceramic Tiles[J]. Nano Energy, 2024, 128: 109928.

    Google Scholar

    [29] GUI Y G, ZHANG W H, LIU S Y, et al. Self-Driven Sensing of Acetylene Powered by a Triboelectric-Electromagnetic Hybrid Generator[J]. Nano Energy, 2024, 124: 109498.

    Google Scholar

    [30] HE S S, GUI Y G, WANG Y F, et al. CuO/TiO2/MXene-Based Sensor and SMS-TENG Array Integrated Inspection Robots for Self-Powered Ethanol Detection and Alarm at Room Temperature[J]. ACS Sensors, 2024, 9(3): 1188-1198.

    Google Scholar

    [31] GUI Y G, HE S S, WANG Y F, et al. MOF-Derived Porous Ni/C Material for High-Performance Hybrid Nanogenerator and Self-Powered Wearable Sensor[J]. Composites Part A: Applied Science and Manufacturing, 2023, 168: 107492.

    Google Scholar

    [32] CHEN H T, SONG Y, GUO H, et al. Hybrid Porous Micro Structured Finger Skin Inspired Self-Powered Electronic Skin System for Pressure Sensing and Sliding Detection[J]. Nano Energy, 2018, 51: 496-503.

    Google Scholar

    [33] YUAN Z Q, SHEN G Z, PAN C F, et al. Flexible Sliding Sensor for Simultaneous Monitoring Deformation and Displacement on a Robotic Hand/Arm[J]. Nano Energy, 2020, 73: 104764.

    Google Scholar

    [34] SHI S, JIANG Y W, XU Q H, et al. A Self-Powered Triboelectric Multi-Information Motion Monitoring Sensor and Its Application in Wireless Real-Time Control[J]. Nano Energy, 2022, 97: 107150.

    Google Scholar

    [35] QIU Y, SUN S S, WANG X E, et al. Nondestructive Identification of Softness via Bioinspired Multisensory Electronic Skins Integrated on a Robotic Hand[J]. NPJ Flexible Electronics, 2022, 6: 45.

    Google Scholar

    [36] GAO S, HAN Q K, JIANG Z Y, et al. Triboelectric Based High-Precision Self-Powering Cage Skidding Sensor and Application on Main Bearing of Jet Engine[J]. Nano Energy, 2022, 99: 107387.

    Google Scholar

    [37] XIE Z J, WANG Y, WU R S, et al. A High-Speed and Long-Life Triboelectric Sensor with Charge Supplement for Monitoring the Speed and Skidding of Rolling Bearing[J]. Nano Energy, 2022, 92: 106747.

    Google Scholar

    [38] PU X J, GUO H Y, TANG Q, et al. Rotation Sensing and Gesture Control of a Robot Joint via Triboelectric Quantization Sensor[J]. Nano Energy, 2018, 54: 453-460.

    Google Scholar

    [39] FRANCOMANO M T, ACCOTO D, GUGLIELMELLI E. Artificial Sense of Slip-A Review[J]. IEEE Sensors Journal, 2013, 13(7): 2489-2498.

    Google Scholar

    [40] GOH Q L, CHEE P S, LIM E H, et al. An AI-Assisted and Self-Powered Smart Robotic Gripper Based on Eco-EGaIn Nanocomposite for Pick-and-Place Operation[J]. Nanomaterials, 2022, 12(8): 1317.

    Google Scholar

    [41] LEI W Q, LU S, WANG Q, et al. A Method of Measuring Weak-Charge of Self-Powered Sensors Based on Triboelectric Nanogenerator[J]. Nano Energy, 2022, 95: 106997.

    Google Scholar

    [42] LU S, LEI W Q, WANG Q, et al. A Novel Approach for Weak Current Signal Processing of Self-Powered Sensor Based on TENG[J]. Nano Energy, 2022, 103: 107728.

    Google Scholar

    [43] NIU S M, WANG S H, LIN L, et al. Theoretical Study of Contact-Mode Triboelectric Nanogenerators as an Effective Power Source[J]. Energy & Environmental Science, 2013, 6(12): 3576-3583.

    Google Scholar

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Preparation and Grasping Application of Self-Powered Tactile Sensor Based on Hybrid Working Mode

    Corresponding author: GUI Yingang ; 

Abstract: 

Triboelectric nanogenerators (TENG) have been widely applied in the field of robot tactile perception. However, the high-impedance output signals pose challenges for portable acquisition and practical applications. At the same time, there is a lack of robot grasping strategies that utilize the working characteristics of triboelectric sensors. To address these challenges, a self-powered tactile sensor that integrated both contact-separation and lateral sliding modes was proposed, enabling tactile perception of contact and sliding through distinct characteristic signals generated by these modes. Furthermore, a signal conditioning circuit that employed a charge-to-voltage conversion method was designed and fabricated, effectively converting high-impedance weak charge signals into low-impedance voltage signals, thus facilitating portable TENG signal detection. Based on this setup, a robot grasping strategy that enabled real-time interaction between the control system and the tactile sensor was proposed, allowing for the determination of the minimum clamping force during the grasping process. This approach enhances the stability of the robot's grasp and reduces potential damage to the object being handled. These advancements contribute to the broader application of triboelectric tactile sensors in robotic grasping systems.

  • 开放科学(资源服务)标识码(OSID):

  • 机器人在工业和服务业领域均有广泛的应用,可以轻松地帮助人类完成简单、重复的工作[1-2]。依靠末端执行器与物体进行接触并完成抓取操作是机器人具备的基本能力之一,安全可靠地抓取需要保证被抓取物体在抓取过程中不会掉落,且被抓取物体不被末端执行器损伤[3-4]。传统的刚性夹持器在抓取物体时容易造成对物体的损伤,而软夹具和触觉感知的结合具有更好的安全性和适应性[5-7]。触觉感知包括对振动、压力、滑动、纹理等信号的感知[8-9],在机器人的抓取过程中,判断末端执行器与被抓取物体是否产生滑动是保证安全抓取的必要条件[10]。在触觉感知中,滑动检测主要通过电容式[11-12]、压电式[13]、光纤布拉格光栅式[14]、摩擦电触觉式[15-16]等传感器或者利用视觉[17-18]进行检测。其中电容式传感器耐久性低,且易受噪音影响;压电式传感器的内阻大,无法测量静态变形,并且温度敏感性不容忽视;光纤布拉格光栅式传感器成本较高,结构复杂;视觉则对光源有依赖[19]。而摩擦电触觉传感器作为自供电传感器,可通过接触带电和静电感应耦合实现信号表征,具有结构简单、成本低、材料丰富等诸多优势[20-25]

    然而,将摩擦纳米发电机(Triboelectric Nanogenerators,TENG)作为触觉传感器还有一些问题需要进一步优化。一方面,TENG的表征信号通常为电压信号、电流信号和电荷信号[26-28],TENG的高阻抗特性使其信号的采集需要专门的测量设备对其进行测量[29-31],使TENG传感器缺乏便捷性,极大地限制了TENG传感器的应用场景。另一方面,大部分TENG传感器采用了单电极和水平滑动的工作模式,通过判断滑动过程产生的脉冲信号实现对滑动或者滑动速度、滑动距离进行监测[32-34],但其信号和功能单一,不能同时对多种动作进行监测。一些开创性的工作证明了将摩擦电传感器作为触觉传感器的可行性[35-38],但在末端执行器的抓取上缺乏一定的应用和拓展,特别是缺乏如何获取抓起物体最小夹持力的策略,减少对被夹持物的损伤。受人在抓取物体时的启示,通过实时判断末端执行器与被夹持物体是否产生相对滑动,实现对物体的自适应抓取,对基于TENG传感器实现不损伤物体的自适应抓取具有重要意义[39-40]。使用电路设计对信号进行放大处理和阻抗转换为便携式检测提供了有效途径,Lei等[41]将微弱的电荷信号转换为可直接测量的小电压信号,Lu等[42]通过将高阻抗的弱电流信号转换为可直接测量的电压信号,实现了对摩擦电传感器的便携式测量。

    本文在以上研究的基础上,提出一种混合工作模式的TENG触觉传感器(MTS-TENG),利用垂直接触分离和水平滑动工作模式下电荷转移的特性,实现在物体抓取过程中对末端执行器与物体接触、分离和滑动的感知。在机器人的抓取过程中,通过MTS-TENG与物体交互得到安全抓取的最小夹持力,实现机器人的自适应抓取。同时通过电荷转电压的方法,设计并制造尺寸为40 mm×45 mm大小的信号检测和处理电路,能够十分便捷和准确地检测到摩擦电触觉传感器的信号,并实时将信号传输到控制系统进行抓取控制以及在上位机展示。

1.   触觉传感器的结构设计与分析
  • TENG的接触分离和水平滑动工作模式具有不同的信号特征,接触分离模式因层间距离迅速变化导致电压变化加快,而水平滑动模式则可控制相对接触面积大小的变化速度进而控制电压变化速度。如图 1a所示,根据不同工作模式的电压信号特征,设计了具备接触分离和滑动感知的MTS-TENG。该传感器由弹性形变材料作为支承,用铜箔作为电极,在两侧电极上用尼龙和聚四氟乙烯分别作为传感器的摩擦材料,其中具有微结构的硅胶薄膜可增大传感器与被抓取物之间的摩擦力。

    摩擦电传感器具有输出高阻抗的特点,不利于信号的检测。如图 1b所示,将传感器的微弱电荷信号通过电荷放大器转化为低电压信号,实现了对传感器信号的阻抗变换,使摩擦电传感器的表征信号能够被普通检测设备检测,不再需要如静电计、示波器等特殊设备进行信号检测,增强了该传感器的便捷性。如图 1c所示,在该工作流程下,机械臂的控制系统可以通过MTS-TENG传递的信号,完成对物体的夹取和释放检测,得到合适的夹持力区间,实现了机器人抓取过程中的反馈控制,有利于工业机器人、服务机器人在精细化操作中的应用。

  • 根据MTS-TENG的结构设计,该传感器可检测接触、滑动和释放3个动作,与TENG的接触分离、水平滑动工作模式所对应。末端执行器在夹持物体的过程中,2种摩擦材料在压力的作用下相互接触,由于接触起电使表面分别带上符号相反的静电荷。当摩擦材料分离时,其表面电荷几乎不会在短时间内消失,在静电感应的作用下,电极上会产生与摩擦材料上相反的静电荷,驱动电子在外电路中流动使2个电极之间产生电势差。当摩擦材料再接触时,2种摩擦材料之间的电势逐渐达到平衡,电荷在外电路中反向移动,电极之间的电势恢复到最初状态。当2种摩擦材料水平滑动时,摩擦材料表面的接触面积减小,摩擦材料表面电荷分离导致电极与摩擦材料之间形成电势差,驱动电荷在外电路中流动,然后2种摩擦材料分离,继续驱动电荷在外电路中转移。以上电荷产生和转移的过程如图 2a所示。为了验证摩擦材料在接触和分离过程中的电势变化趋势,利用有限元软件对接触分离、水平滑动2种工作模式进行仿真,图 2b展示了2种工作模式的仿真结果。2种工作模式的电荷转移测试结果如图 2c所示,当摩擦材料在压力的作用下相互接触时,电荷在极短时间内发生转移;当摩擦材料产生相对滑动时,电荷往相反方向转移,摩擦材料停止滑动,电荷也停止转移;当摩擦材料分离时,电荷继续在短时间内发生转移,恢复到最初水平。根据接触分离式TENG的V-Q-x关系式[43],电极之间的感应电势差与层间距离和转移电荷有关,2种工作模式仿真结果的电势变化趋势和电荷测试结果一致。

    尺寸大小为40 mm×60 mm的MTS-TENG电极之间的电势差测试结果如图 3a所示,传感器与物体接触时,在压力的作用下传感器内的2种摩擦材料相互接触,此时电压在短时间内变化到-45 V;摩擦材料相互滑动时,电压随着电荷的转移逐渐增加至-20 V,停止滑动时电压不再变化;当压力释放时摩擦材料相互分离,电压增加至11 V。上述过程与有限元仿真结果所表现的电势变化趋势一致。可以看出,当传感器与物体接触时,电压将迅速变化到较低水平;当传感器与物体分离时,电压恢复到最初水平,此时可根据电压的变化判断传感器与物体的接触状态。由图 3b可知,当传感器与物体接触时,电压在25 ms内完成变化,当物体与传感器分离时,电压在170 ms内完成变化(这是因为在分离时,仅利用弹性材料的弹性形变使2种摩擦材料分离,其分离速度较慢导致)。从前面的分析可以看出,接触过程和滑动过程中电极之间的电势变化速度有所不同,仅仅通过电压数值的变化来判断是否产生相对滑动具有较大的局限性,故需要通过实时计算电压变化率K来对抓取过程中是否产生相对滑动进行判断,电压变化率的计算公式为:

    图 3c展示了电压变化率K的计算结果,从图中可以看出在MTS-TENG接触和分离的过程中,K分别为-52 V/s和60 V/s。MTS-TENG的摩擦材料产生相对滑动的K的计算结果如图 3d所示,可以看出相对静止时K趋近于0 V/s,在相对滑动的过程中K保持在0.7 V/s以上,故可以通过计算K的大小准确判断MTS-TENG在抓取过程中的状态。同时,使用尺寸大小为30 mm×50 mm的MTS-TENG测试了不同滑动距离下的电压变化情况,如图 3e所示,分别测试了5 mm、10 mm、15 mm、20 mm、25 mm共5组滑动距离所对应的电压,并在图中展示了良好的线性关系。图 3f展示了不同滑动速度与K的关系,同样具备良好的线性度。

2.   便携式采集电路设计和自适应抓取
  • 将TENG作为自供电传感器时,其输出的电压、电流、电荷信号均可作为表征信号,但3种信号都具有高阻抗的特点,其中电荷信号一般为nC级,是一种微弱的电荷信号。当使用电荷信号作为表征信号时,可以将TENG等效为一个电容量为Cy的电容器和与之并联的电荷源qc,通过利用电荷放大器可以将弱电荷信号转换为低电压信号。理想情况下的原理图如图 4a所示,由于运算放大器的直流输入电阻很高,故TENG的输出电荷Q只对电容Cf充电,则电荷放大器的输出电压Uo即为电容Cf上的电压Uc,如式(2)所示。同时为防止因Cf长期充电导致集成运放饱和,引入反馈电阻Rf提高放大器的工作稳定性。

    由于电荷放大器对微弱电荷具有很强的检测能力,在对传感器的电荷信号进行检测时,不可避免会受环境中的干扰因素影响,为了防止传感器信号在传输过程中受到各种信号和辐射的干扰,需要增加对信号调理电路的设计。如图 4b所示,通过增加同相放大电路对电荷放大器所转换的电压信号进行放大和增强,提升传感器信号的抗干扰能力。由于传感器在工作时的频率不超过10 Hz,同时为排除来自电源和环境中50 Hz工频信号的干扰,设计了基于有限电压源法的二阶低通滤波器,能够有效滤除频率超过10 Hz的信号,减少对传感器信号的干扰。如图 4c所示,为了实现传感器与控制系统之间的交互和实时控制,增加了单片机和蓝牙模块对传感器信号进行检测和传输。将上述信号调理和检测电路通过PCB设计集成在1块40 mm×45 mm的电路板上,实现对MTS-TENG电荷信号进行采集和传输的便携化。图 4d展示了通过上述电路板采集和传输到电脑上的传感器数据,验证了该电路板的可行性。

  • 为了实现末端执行器的自适应抓取,设计了如图 5a所示的抓取步骤。在末端执行器的抓取过程中,将MTS-TENG一侧固定在末端执行器的内侧,另一侧与被夹取物体接触。第1步通过MTS-TENG接触时的电压信号判断末端执行器是否与物体接触,当控制系统得到与物体接触的信号以后,执行第2步操作,即末端执行器带动MTS-TENG向上举升。由于MTS-TENG与物体接触一侧具有微结构的硅胶薄膜,使物体表面和传感器外侧的摩擦系数远大于传感器内部2个摩擦层之间的摩擦系数,摩擦层之间发生相对滑动。举升过程中末端执行器逐渐增加对物体的压力,从而使摩擦层之间的摩擦力逐渐克服物体的重力,当MTS-TENG检测到摩擦材料之间相对滑动停止,此时末端执行器的正压力是能够抓起物体的最小夹持力。末端执行器释放物体时,MTS-TENG在弹性材料的作用下恢复到最初状态。通过该抓取流程,实现了机器人在没有预先设置夹取正压力的情况下,通过MTS-TENG与控制系统的交互完成自适应的抓取,能最大限度防止由于夹持力太大对被夹持物造成的破坏。如图 5b所示,使用25 mm×50 mm尺寸大小的MTS-TENG探究了在1 N、2 N、5 N、10 N共4种不同正压力下滑动时的电压差和电压变化率K,可以看出K和电压差先增大后减小,这是因为正压力的逐渐增大可以在微观上增强摩擦材料之间的摩擦力,从而产生更多的感应电荷。而过大的正压力使相对滑动的速度减小,单位时间内转移的电荷下降,导致输出的电压减小。图 5c展示了滑动过程中正压力逐渐从0 N增加到12 N时电压的变化(图中红色曲线上升段)以及计算得到的电压变化率K,在整个滑动过程中,K始终维持在大于0 V/s的水平,验证了在抓取过程中压力逐渐增大时对滑动的检测能力。

3.   信号调理电路测试与抓取应用
  • 为了实现抓取过程中信号的判断和状态的识别,对信号调理电路进行了测试。图 6a展示了信号调理电路检测MTS-TENG滑动时的信号,可以看出信号调理电路的抗干扰和滤波能力,能够有效滤除包括工频信号等的干扰。图 6b展示了信号调理电路对整个抓取过程中的信号进行重复测试,根据测试结果计算了该信号的实时电压变化率K,如图 6c所示,在接触时K从0 V/s变为-4.5 V/s后恢复到0 V/s,滑动时K从0 V/s增加并保持在0.16 V/s以上。为了使控制系统能更好识别MTS-TENG信号,对采集的信号根据不同动作的特征进行预处理,如图 6d所示。当接触、分离时单片机输出信号为2,滑动时则输出信号为1。图 6e图 6f展示了MTS-TENG完成接触、分离状态切换时分别需要80 ms和170 ms。

    为了验证设计的MTS-TENG和信号调理电路在自适应抓取中的应用效果,搭建了如图 6g所示的抓取测试平台。使用舵机控制末端执行器的夹取和释放;通过步进电机控制丝杆滑台使末端执行器能够垂直上移和下降,以此来模拟末端执行器的抓取操作;通过信号调理电路对MTS-TENG的信号进行识别和传输,并在PC端或手机端上显示。根据末端执行器接触面的大小,制造了尺寸为30 mm×50 mm的MTS-TENG,将其粘贴在末端执行器接触面。如图 6h所示,在末端执行器与铝型材接触过程中,电压从0 V变化到2 V,此时控制步进电机使末端执行器向上移动,同时增加末端执行器的夹持力,使MTS-TENG的摩擦材料相对滑动。当停止相对滑动时,末端执行器不再增加夹持力,并且铝型材随着末端执行器一起向上移动,在这个过程中MTS-TENG的电压信号从缓慢下降到维持不变。当末端执行器释放铝型材时,MTS-TENG在弹性材料的作用下恢复到最初状态,电压信号也同时完成对应的变化。MTS-TENG和信号调理电路在抓取时实时检测末端执行器与铝型材的状态,通过接触检测、滑动检测,得到了能够抓取该铝型材的最小夹持力,实现了对铝型材的自适应抓取和释放,验证了该抓取策略的可行性,同时也证明了该信号调理电路在信号检测、传输方面的良好性能。

4.   结论
  • 本文设计制造了可用于检测和传输TENG输出高阻抗和弱电荷信号的调理电路,提出了一种通过控制系统与触觉传感器在抓取过程中进行交互并完成自适应抓取的策略,实现了对末端执行器在抓取物体时最小夹持力的判定。该触觉传感器将TENG接触分离和水平滑动2种工作模式进行组合,通过2种工作模式表征信号的不同,完成接触、分离和滑动的检测。不同表征信号的反馈使控制系统能够实时对夹持力进行控制,以抓取易损的物品。实验结果表明:通过对电压变化率K的计算可以准确判断是否产生相对滑动,同时也可以对滑动速度、滑动距离进行判断和识别;信号调理电路集成了信号检测、处理和传输的功能,具备较快的响应速度(不超过200 ms)和较高的检测精度,可用于将电荷信号作为表征信号的传感器进行信号检测。基于上述结果,信号调理电路有利于增强TENG作为传感器的应用,可以充分发挥TENG自供电、易于制造、成本低、组成材料选择丰富的特点,该触觉传感器和抓取策略则进一步拓展了摩擦电传感器技术在机器人中的潜在应用,特别是为机器人自适应抓取的应用提供了重要的参考。

Figure (6)  Reference (43)

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