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It is important to monitor wrist four direction movements (flexion, extension, adduction and abduction) for hand healthcare, wrist rehabilitation and upper limb exercise, and so on. The purpose of this study is to develop a quadri-directional optical bending sensor that integrated wearable device technology in a smart glove to detect wrist four direction movements.

The quadri-directional optical bending sensor was designed with a microcontroller board, a Bluetooth wireless module, a side-emitting polymeric optical fibre (POF), an infrared light emitting diode and four phototransistors. A linear equation was deduced to calculate bending angle from detecting sensor value of Arduino microcontroller. The bending angle values could be seen by the smartphone screen, so the system has a good human–machine interface function.

The light emission by macro-bending of the side-emitting POFs that the transmittance of the outer side is greater than the inner. The bending POFs lateral emission phenomenon integrated with phototransistors on the edge is suitable for the development of bending sensors.

This study is to develop a novel quadri-directional optical bending sensor to replace two bi-direction sensors or four uni-direction sensors for wrist four direction movements monitoring.

The structural adaptive ability of the soft robot is fully demonstrated in the grasping task of the soft hand. A soft hand can easily realize the envelope operation of the object without planning. With the continuous development of robot applications, researchers are no longer satisfied with the ability of the soft hand to grasp. The purpose of this paper is to perceive the object’s shape while grasping to provide a decision-making basis for more intelligent robot applications.

This paper proposes a dual-signal comparison method to obtain the fingertip position. The dual signal includes the displacement calculated by the static model without considering the external load change and the displacement calculated by the bending sensor. The dual-signal comparison method can use the obvious change trend difference between the above two signals in the hover and contact states to identify the touch position. The authors make the soft hand scan around the object through touch operation to detect the object’s shape, and the tracks of every touch fingertip position can envelop the object’s shape.

The experimental results show that the dual-signal comparison method can accurately identify the contact moment of soft fingers. This detection method makes the soft hand develop the shape detection ability. The soft hand in the experiment can perceive squares, circles and a few other complex shapes.

The dual-signal comparison method proposed in this paper can detect a touch action by using the signal change trend when the working condition suddenly changes with the rough robotic model and sensing, thus improving the utilization value of the measured signal. The problems of large model errors and inaccurate sensors also negatively impact the use of other soft robots. It is generally difficult to achieve good results by directly using these models and sensors with the thinking of rigid robot analysis. The dual-signal comparison method in this paper can provide some reference for this aspect.

The aim of the paper is to propose a global, automated and continuous curvature calibration strategy for bending sensors, which is used for the angle feedback control of soft fingers.

In this work, the proposed curvature calibration strategy for bending sensors is based on the constant curvature bending properties of soft fingers. The strategy is to install the bending sensor on the soft finger and use the laser distance sensor to assist calibration, then calculate the relationship between the curvature and the voltage of the bending sensor through geometric conversion. In addition, this work also develops a full set of standard calibration systems and collection procedures for the bending sensor curvature calibration and uses machine learning algorithms to fit the collected data.

First, compared with the traditional calibration methods, the proposed curvature calibration strategy can achieve constant curvature measurement with the advantages of better continuity. Second, using the sensor data obtained by the proposed calibration method as the feedback signal for the soft finger bending angle control, the control effect is better than that of the traditional method.

This work proposes and verifies a global, automated and continuous curvature calibration strategy for bending sensors and is used for the angle feedback control of soft fingers. In addition, this work also develops a full set of standard calibration systems and collection procedures, which can be applied to a variety of flexible bending sensors with a good adaptability.

The purpose of this paper is to introduce a variable distance pneumatic gripper with embedded flexible sensors, which can effectively grasp fragile and flexible objects.

Based on the motion principle of the three-jaw chuck and the pneumatic “fast pneumatic network” (FPN), a variable distance pneumatic holder embedded with a flexible sensor is designed. A structural design plan and preparation process of a soft driver is proposed, using carbon nanotubes as filler in a polyurethane (PU) sponge. A flexible bending sensor based on carbon nanotube materials was produced. A static model of the soft driver cavity was established, and a bending simulation was performed. Based on the designed variable distance soft pneumatic gripper, a real-time monitoring and control system was developed. Combined with the developed pneumatic control system, gripping experiments on objects of different shapes and easily deformable and fragile objects were conducted.

In this paper, a variable-distance pneumatic gripper embedded with a flexible sensor was designed, and a control system for real-time monitoring and multi-terminal input was developed. Combined with the developed pneumatic control system, a measure was carried out to measure the relationship between the bending angle, output force and air pressure of the soft driver. Flexible bending sensor performance test. The gripper diameter and gripping weight were tested, and the maximum gripping diameter was determined to be 182 mm, the maximum gripping weight was approximately 900 g and the average measurement error of the bending sensor was 5.91%. Objects of different shapes and easily deformable and fragile objects were tested.

Based on the motion principle of the three-jaw chuck and the pneumatic FPN, a variable distance pneumatic gripper with embedded flexible sensors is proposed by using the method of layered and step-by-step preparation. The authors studied the gripper structure design, simulation analysis, prototype preparation, control system construction and experimental testing. The results show that the designed flexible pneumatic gripper with variable distance can grasp common objects.

The purpose of this paper is to compare the sensing characteristics of uniform fiber Bragg grating (FBG) and tilted fiber Bragg grating (TFBG) by presenting a detailed research review. Temperature, axial strain, bending, vibration and refractive index measurands of FBG and TFBG sensor are presented and some significant differences are found.

Theoretical analysis and practical application in engineering are investigated and compared from other authors' research papers and self analysis. Spectra behavior of both FBG and TFBG are discussed.

There are found to be significant differences in temperature, axial strain, bending, vibration and refractive index sensing characteristics of FBG and TFBG.

The paper's analysis is comprehensive and clear and provides readers with the sensing characteristics of FBG and TFBG in detail.

With the increasing development of the surgical robots, the opto-mechatronic technologies are more potential in the robotics system optimization. The optic signal plays an important role in opto-mechatronic systems. This paper aims to present a review of the research status on fiber-optic-based force and shape sensors in surgical robots.

Advances of fiber-optic-based force and shape sensing techniques in the past 20 years are investigated and summarized according to different surgical requirement and technical characteristics. The research status analysis and development prospects are discussed.

Compared with traditional electrical signal conduction, the phototransduction provides higher speed transmission, lower signal loss and the immunity to electromagnetic interference in robot perception. Most importantly, more and more advanced optic-based sensing technologies are applied to medical robots in the past two decades because the prominence is magnetic resonance imaging compatibility. For medical robots especially, fiber-optic sensing technologies can improve working security, manipulating accuracy and provide force and shape feedback to surgeon.

This is a new perspective. This paper mainly researches the application of optical fiber sensor according to different surgeries which is beneficial to learn the great potential of optical fiber sensor in surgical robots. By enumerating the research progress of medical robots in optimization design, multimode sensing and advanced materials, the development tendency of fiber-optic-based force and shape sensing technologies in surgical robots is prospected.

Flexible pressure sensor arrays have promising applications in analog haptics, reconfiguration of sensory functions, artificial intelligence, wearable devices and human-computer interaction. The force disturbance generated by the connecting material between the sensor array units will reduce the detection accuracy of the unit. The purpose of this paper is to propose a flexible pressure sensor with interference immunity capability. A C-type bridge flexible piezoelectric structure is used to improve the pressure perturbation. The interference immunity capability of the sensor has been improved.

In this paper, a C-type pressure sensor array structure by rapid injection moulding is manufactured through the positive piezoelectric effect of a piezoelectric material. The feasibility of C-type interference immunity structure in a flexible sensor array is verified by further analysis and experiment. A flexible pressure sensor array with C-type interference immunity structure has been proposed.

In this paper, we present the results of the perturbation experiment results of the C-type pressure sensor array, showing that the perturbation error is less than 8%. The test of the flexible sensor array show that the sensor can identify the curved angle of up to 120 °, and the output sensitivity of the sensor in the horizontal state reaches 0.12 V/N, and the sensor can withstand the pressure of 80 N. The flexible sensor can work stably in the stretch rate range of 0–8.6% and the stretch length range of 0–6 mm.

In this paper, C-type pressure sensor array structure is fabricated by rapid injection moulding for the first time. The research in this paper can effectively reduce the disturbance of input pressure on the sensor’s internal array and improve the output accuracy. The sensor can intuitively reflect the number of fingers sliding on the sensor by the order in which the maximum voltage appears. Due to the strong interference immunity capability and flexibility of the flexible sensor array mechanism, it has a broad application prospect in the practical fields of haptic simulation, perceptual function reconstruction, artificial intelligence, wearable devices and human–computer interaction.

This paper aims to review the shape sensing techniques using large area flexible electronics (LAFE). Shape perception of humanoid robots using tactile data is mainly focused.

Research papers on different shape sensing methodologies of objects with large area, published in the past 15 years, are reviewed with emphasis on contact-based shape sensors. Fiber optics based shape sensing methodology is discussed for comparison purpose.

LAFE-based shape sensors of humanoid robots incorporating advanced computational data handling techniques such as neural networks and machine learning (ML) algorithms are observed to give results with best resolution in 3D shape reconstruction.

The literature review is limited to shape sensing application either two- or three-dimensional (3D) LAFE. Optical shape sensing is briefly discussed which is widely used for small area. Optical scanners provide the best 3D shape reconstruction in the noncontact-based shape sensing; here this paper focuses only on contact-based shape sensing.

Contact-based shape sensing using polymer nanocomposites is a very economical solution as compared to optical 3D scanners. Although optical 3D scanners can provide a high resolution and fast scan of the 3D shape of the object, they require line of sight and complex image reconstruction algorithms. Using LAFE larger objects can be scanned with ML and basic electronic circuitory, which reduces the price hugely.

LAFE can be used as a wearable sensor to monitor critical biological parameters. They can be used to detect shape of large body parts and aid in designing prosthetic devices. Tactile sensing in humanoid robots is accomplished by electronic skin of the robot which is a prime example of human–machine interface at workplace.

This paper reviews a unique feature of LAFE in shape sensing of large area objects. It provides insights from mechanical, electrical, hardware and software perspective in the sensor design. The most suitable approach for large object shape sensing using LAFE is also suggested.

This paper describes SHAPE TAPE™, a thin array of fiber optic curvature sensors laminated on a ribbon substrate, arranged to sense bend and twist. The resulting signals are used to build a three dimensional computer model containing six degree of freedom position and orientation information for any location along the ribbon. The tape can be used to derive dynamic or static shape information from objects to which it is attached or scanned over. This is particularly useful where attachment is only partial, since shape tape “knows where it is” relative to a starting location. Measurements can be performed where cameras cannot see, without the use of magnetic fields. Applications include simulation, film animation, computer aided design, robotics, biomechanics, and crash testing.

The purpose of this paper is to present a novel data glove which can capture the motion of the arm and hand by inertial and magnetic sensors. The proposed data glove is used to provide the information of the gestures and teleoperate the robotic arm-hand.

The data glove comprises 18 low-cost inertial and magnetic measurement units (IMMUs) which not only make up the drawbacks of traditional data glove that only captures the incomplete gesture information but also provide a novel scheme of the robotic arm-hand teleoperation. The IMMUs are compact and small enough to wear on the upper arm, forearm, palm and fingers. The calibration method is proposed to improve the accuracy of measurements of units, and the orientations of each IMMU are estimated by a two-step optimal filter. The kinematic models of the arm, hand and fingers are integrated into the entire system to capture the motion gesture. A positon algorithm is also deduced to compute the positions of fingertips. With the proposed data glove, the robotic arm-hand can be teleoperated by the human arm, palm and fingers, thus establishing a novel robotic arm-hand teleoperation scheme.

Experimental results show that the proposed data glove can accurately and fully capture the fine gesture. Using the proposed data glove as the multiple input device has also proved to be a suitable teleoperating robotic arm-hand system.

Integrated with 18 low-cost and miniature IMMUs, the proposed data glove can give more information of the gesture than existing devices. Meanwhile, the proposed algorithms for motion capture determine the superior results. Furthermore, the accurately captured gestures can efficiently facilitate a novel teleoperation scheme to teleoperate the robotic arm-hand.