Dong-Yeop Seok

Capacitive sensors are widely adopted in compact robotic systems due to their simple structure, ease of fabrication, and scalability for miniaturized designs. However, sensor miniaturization inevitably leads to reduced sensitivity and increased sensitivity imbalance, particularly in torque measurements, due to limited electrode area and spatial constraints. To address these limitations, this paper presents a compact six-axis force/torque (F/T) sensor based on a redundant capacitive sensing architecture. The proposed sensing architecture employs a symmetric arrangement of multiple capacitive electrodes, providing redundant capacitance measurements that enhance sensitivity while reducing coupling errors under multi-axis loading conditions. By exploiting redundant capacitive responses rather than relying on complex mechanical separation, the proposed design effectively improves measurement robustness. Based on this architecture, a compact six-axis F/T sensor with a diameter of 20 mm and a height of 12 mm is developed. Experimental validation demonstrates that the proposed sensor achieves linearity (>98.2%) with reduced cross-axis interference, confirming improved sensitivity and reliable multi-axis F/T measurement. This work provides a practical and scalable solution for integrating high-performance six-axis F/T sensing into space-constrained robotic systems.

A capacitive six-axis force/torque (F/T) sensor has favorable characteristics for miniature design. However, when designing small-sized force/torque sensors, anisotropy among the six axes can lead to uneven sensitivity across each axis. This is due to increased crosstalk errors, which degrade sensor performance. To design a miniature six-axis force/torque sensor, it is essential to analyze the isotropic relationships between the six-axis forces/torques and the capacitance change to reduce crosstalk errors. This paper presents a miniature capacitive six-axis F/T sensor optimized for isotropy. It also establishes a systematic method for designing sensing electrodes. The sensor’s deformable structure is analyzed using Castigliano’s beam theory, and design parameters are optimized with isotropy analysis of the deformable part. The criteria are also presented, including selecting the electrode area and initial gap using linear equations derived from capacitance change analysis. The optimized miniature F/T sensor is calibrated using a neural network-based calibration method, and its accuracy errors are compared to a reference sensor. The design framework provides a foundation for future developments in miniature sensors.

The soft robot manipulator is attracting attention in the surgical fields with its intrinsic softness, lightness in its weight, and safety toward the human organ. However, it cannot be used widely because of its difficulty of control. To control a soft robot manipulator accurately, shape sensing is essential. This paper presents a method of estimating the shape of a soft robot manipulator by using a skin-type stretchable sensor composed of a multiwalled carbon nanotube (MWCNT) and silicone (p7670). The sensor can be easily fabricated and applied by simply attaching it to the surface of the soft manipulator. In its fabrication, MWCNT is sprayed on a teflon sheet, and liquid-state silicone is poured on it. After curing, we turn it over and cover it with another silicone layer. The sensor is fabricated with a sandwich structure to decrease the hysteresis of the sensor. After calibration and determining the relationship between the resistance of the sensor and the strain, three sensors are attached at 120° intervals. Using the obtained data, the curvature of the manipulator is calculated, and the entire shape is reconstructed. To validate its accuracy, the estimated shape is compared with the camera data. We experiment with three, six, and nine sensors attached, and the result of the error of shape estimation is compared. As a result, the minimum tip position error is approximately 8.9 mm, which corresponded to 4.45% of the total length of the manipulator when using nine sensors.

This article proposes a method for improving the resolution of a capacitive-type (C-type) torque sensor. This method amplifies the output signal variation of a capacitance-to-digital (CDC) chip by designing a series-resonant circuit. This study considers the data sampling method of the CDC chip and the effects of the resonant circuit on the output signal. The CDC chip employs a switched-capacitor (SC) integrator for data sampling. The SC integrator was simulated to estimate the amplification of the output signal by the resonant circuit. The inductance and resistance of the resonant circuit are designed to optimize the sensor performance. As the SC integrator is commonly used in CDC chips, the proposed method is adaptable to a majority of existing C-type sensors. When fabricating the sensor, it is important to obtain the designed capacitance for implementing the resonant circuit. We design printed circuit board structures that can accurately obtain the designed gap between sensor electrodes and the ground. Artificial neural network is used for sensor calibration process. To prove the effectiveness of these methods, the performance of a sensor equipped with the resonant circuit was compared to that of a previously developed sensor.

Force/Torque (F/T) sensing technology enables a dexterous robot control such as direct teaching, master-slave system, and pick-and-place task. In general, 6-axis F/T sensor is attached to the end-effector of the robot manipulator to assist in utilizing advanced robot systems. However, in actual applications, various tools such as robotic grippers, robotic hand, grinders are attached to the sensor and it causes F/T offset} with respect to the gravity. In this letter, Autonomous Weight Compensating(AWC) technique for 6-axis F/T sensor is presented. The proposed AWC technique can reduce the F/T offsets by estimating the F/T offsets through installed Inertial Measurement Unit(IMU) sensor. In this study, the 6-axis F/T are measured based on capacitance sensing scheme and to estimate the orientation of the sensor, a 9-axis IMU sensor is installed inside of the sensor. Then, the F/T offsets are calibrated via Artificial Neural Network(ANN) model. As a result, the sensor can reduce the F/T offsets without any additional computing time in robot operating period. Finally, the performance of the proposed method is demonstrated through comparing the F/T data with both trained data and untrained data.

As advanced robotic technologies such as human-robot interaction and automatic assembly processes have emerged, torque sensors have become an essential component for robots. However, commercial torque sensors are not suitable for robotic applications because of their large sizes, heavy weights, narrow options, and high prices. In this letter, we develop a novel capacitive joint torque sensor with ultra-thin structure, high performance, but low cost. To achieve these goals, novel designs have been applied to both the sensing and deformable parts, which are the most important elements of the torque sensor. To obtain high sensitivity, a novel electrode structure called wedge electrode was applied to the sensing part, and a new deformable structure was designed to be ultra-thin and easy to manufacture. Then, the electrode and deformable structures were implemented in a single torque sensor. The developed torque sensor was calibrated based on an artificial neural network (ANN) model and verified to perform high accuracy and sensitivity, and low crosstalk by comparing it with a commercial torque sensor. Finally, a high-performance torque sensor was implemented in ultra-thin size with a diameter of 108 mm and a thickness of 13 mm.

This paper presents an overview of AiDIN-VI, a force-controllable quadruped robot system with incorporated mandatory abilities of speed, efficiency, and mobility for providing real-world services. This paper describes the design methodologies and principles employed to implement these requisite capabilities on a single robot platform; particularly, the torque sensing method, along with the components and modularization method of the torque-controllable actuator unit are elucidated herein. The developed robot platform is equipped with all necessary components, including onboard PCs (for motion generation and vision mapping), a battery, modular actuator units, a wireless network router, and a remote e-stop controller for autonomous and manual locomotion control. The robot is subdivided into joints, legs, and robots, and its performance is experimentally tested. The capability of the robot with regard to joint torque control (± 70 Nm), leg force control (350 N along the z-axis), robot (vertical loading 25 kg, pulling force 200 N) were experimentally verified, and locomotive performances (walk, pace, trot, and jump) on various terrains were executed. A maximum trot gait speed of 1.2 m/s was recorded, along with minimum costs of transport of 1.187 and 1.15 at a speed of 1.0 m/s and under loading conditions, respectively.