An AI-Assisted and Self-Powered Smart Robotic Gripper Based on Eco-EGaIn Nanocomposite for Pick-and-Place Operation

Goh, Qi-Lun; Chee, Pei Song; Lim, Eng-Hock; Ng, Danny Wee-Kiat

doi:10.3390/nano12081317

Cited by 13 publications

(3 citation statements)

References 45 publications

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“…In scenarios with a limited number of samples featuring distinguishable characteristics, SVM holds a significant advantage. Therefore, it can assist the TENG sensors in achieving simple object and character recognition [ 116 , 117 , 118 ]. Recently, Zhao et al designed an untethered triboelectric patch for intelligent sensing and energy harvesting [ 67 ].…”

Section: ML For Tengsmentioning

confidence: 99%

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

Li,

Wei,

Wang

2024

Nanomaterials

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The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase the economic burden. The triboelectric nanogenerators (TENGs) provide both an energy harvesting scheme and the possibility of self-powered sensing. Based on contact electrification from different materials, TENGs provide a rich material selection to collect complex and diverse data. As the data collected by TENGs become increasingly numerous and complex, different approaches to machine learning (ML) and deep learning (DL) algorithms have been proposed to efficiently process output signals. In this paper, the latest advances in ML algorithms assisting solid–solid TENG and liquid–solid TENG sensors are reviewed based on the sample size and complexity of the data. The pros and cons of various algorithms are analyzed and application scenarios of various TENG sensing systems are presented. The prospects of synergizing hardware (TENG sensors) with software (ML algorithms) in a complex environment and their main challenges for future developments are discussed.

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Section: ML For Tengsmentioning

confidence: 99%

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

Li,

Wei,

Wang

2024

Nanomaterials

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“…A voltage can only be produced due to the IPMC deformation (bending or compression), giving it advanced passive output performance that is not possible in the conventional strain sensors, which can be mainly categorized as resistive, capacitive, and inductive. Self-powered mechanisms such as electromagnetic induction, triboelectric, and piezoelectric nanogenerators that convert mechanical energy into electrical energy have been explored as sensors [37][38][39][40][41][42]. However, unlike IPMC, these methods can only generate instant electrical output at the transient state (dynamic response).…”

Section: Introductionmentioning

confidence: 99%

Kirigami-inspired self-powered pressure sensor based on shape fixation treatment in IPMC material

Low,

Chee,

Lim

et al. 2024

Smart Mater. Struct.

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Rapid advances in sensing technologies have brought about the fast development of wearable electronics for biomedical applications. Since its conception, over the years, the ionic polymer metal composite (IPMC) is a new man-made material that has demonstrated its great potential for wearable devices due to self-powered sensing capabilities. Here, for the first time, a novel Kirigami technique with unique cut patterns has been employed for designing a stretchable IPMC sensor with enhanced performance. As Nafion itself exhibits the characteristic of shape memory polymer, the Kirigami structure that is built using the IPMC can be buckled up by loading and heating the IPMC above the deformation temperature, Tdef. To further enhance the memory effect, the Kirigami structure has further been locked by immersing it in potassium hydroxide (KOH) for the formation of deprotonated Nafion. The voltage output of the proposed IPMC with Kirigami shows a superior performance with 3 times improvement over the conventionally planar electrodes. Dynamic tests with a range of displacements have been performed to validate the sensor design and the robustness of the Kirigami structure. This novel Kirigami-based IPMC sensor has been successfully demonstrated for braille sensing by designing 7 independent electrodes.

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“…A data glove consists of numerous integrated sensors that enable various applications, such as human-machine interaction, virtual reality (VR) object manipulation, object recognition, gesture recognition, patient rehabilitation, virtual surgery, and entertainment [1][2][3][4]. The sensors are made of stretchable polymers, including Ecoflex [5,6] and polydimethylsiloxane (PDMS) [7,8], to ensure their compatibility with the data glove. For VR applications, the data glove enables the manipulation of the object in a virtual environment by detecting the degree of freedom (DOF) of the hand movement [9,10].…”

Section: Introductionmentioning

confidence: 99%

Data Glove with Integrated Polyethylene‐Carbon Composite‐Based Strain Sensor for Virtual Reality Applications

et al. 2023

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A data glove enables fine motion control in virtual reality (VR). This work presents a cost‐effective data glove made of a commercial nylon‐based glove with integrated polyethylene‐carbon composites (Velostat). The resistance of the Velostat varies when a finger is bent. This piezoresistive behavior was explored by designing the Velostat as the strain sensor. The strain sensor was characterized for its flexure sensing by connecting it to a data acquisition circuit. The circuit is designed to process the output of the joint angles and feed it to the computer. A linear resistance output was measured with a sensitivity/gauge factor of 1.45 % per degree with a response time of 0.0158 seconds when the strain sensor bends from 0° to 30°. To control the 3D virtual hand's movement, the data glove was coupled with two inertial measurement unit sensors at the forearm and upper arm to identify its coordinates. The fabricated data glove successfully performs a proof‐of‐concept by picking‐and‐placing multiple objects in a VR environment.

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An AI-Assisted and Self-Powered Smart Robotic Gripper Based on Eco-EGaIn Nanocomposite for Pick-and-Place Operation

Cited by 13 publications

References 45 publications

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

Kirigami-inspired self-powered pressure sensor based on shape fixation treatment in IPMC material

Data Glove with Integrated Polyethylene‐Carbon Composite‐Based Strain Sensor for Virtual Reality Applications

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