To manipulate objects using a robot hand, it is important to measure the information of the various forces on the fingertips. In this letter, a six-axis force/torque (F/T) fingertip sensor for a robot hand is introduced. The sensor was developed to provide the ability to measure six-axis F/T while remaining feasible for robot fingertip integration because of its miniaturization, light weight, and low cost (thanks to its simple manufacturing process). In particular, a novel highly sensitive shear force measurement method is proposed that uses the eccentricity of two cylinders. The designed six-axis F/T sensor was also fabricated. We demonstrate that the developed sensor can be easily installed into the human-sized fingertip of a robot, and we performed an accuracy evaluation using several experiments.

In the field of robotics, sensors are crucial in enabling the interaction between robots and their users. To ensure this interaction, sensors mainly measure the user’s strength, and based on this, wearable robots are controlled. In this paper, we propose a novel three-axis force/torque sensor for wearable robots that is compact and has a high load capacity. The bolt and nut combination of the proposed sensor is designed to measure high-load weights, and the simple structure of this combination allows the sensor to be compact and light. Additionally, to measure the three-axis force/torque, we design three capacitance-sensing cells. These cells are arranged in parallel to measure the difference in capacitance between the positive and negative electrodes. From the capacitance change measured by these sensing cells, force/torque information is converted through deep neural network calibration. The sensing point can also be confirmed using the geometric and kinematic relation of the sensor. The proposed sensor is manufactured through a simple and inexpensive process using cheap and simply structured components. The performance of the sensor, such as its repeatability and capacity, is evaluated using several experimental setups. In addition, the sensor is applied to a wearable robot to measure the force of an artificial muscle.

Compliance devices facilitate robotic assemblies by preventing excessive contact force on automated devices. However, the unavailability of displacement information regarding compliance devices makes it difficult to protect robots and objects during robotic assembly. It also limits repeatability and efficiency, especially regarding peg-in-hole and similar operations. We propose a six-axis displacement sensor installed on a remote center compliance device to detect the three translation and three rotation components generated by external forces and torques applied to the device, respectively. Triangular prism-shaped structures are used with capacitive sensing technology to measure the vertical and horizontal displacements in mm scale generated by the external normal and shear forces applied to the sensor. We designed and fabricated the sensor that could be integrated into a remote center compliance device, demonstrating it has a simple structure and is easy to install. To obtain six-axis force/torque information and six-axis displacement, calibration experiments were conducted using a specific setup and considering the device's geometry. The high sensing performance was verified through complementary experiments.

This article presents a gripper comprising finger and suction with reconfigurable attributes. With the reconfiguration feature, the proposed gripper has a configuration suitable for different working environments of logistic order picking. The finger part of the gripper was configured with the parallelogram remote center of motion (RCM) mechanism to implement reconfigurable features. With the RCM mechanism, the gripper implements the function of zeroed offset, which removes the gap between the finger and the suction gripper, and the function of the supporting finger. In order-picking tasks, the gripper shows higher grasping stability and practicality than existing grippers. First, the design of the mechanism and model constituting the gripper is described. Afterward, a quantitative evaluation of the performance of this gripper compared to the existing ones in the bin and shelf environment is conducted. In this section, the gripper shows 32.912% improved performance in representative tasks. Finally, the practical aspects of this gripper are described through a quantitative evaluation.

Wearable robots require compact sensing interfaces to monitor the forces transmitted through attachment straps. This study aims to combine strap fastening and force measurement in a lightweight buckle, enabling the magnitude and direction of strap loading to be monitored at the attachment interface. The proposed buckle incorporates two capacitive sensing cells and a flexure structure that converts applied loads into measurable capacitance changes. Their combined responses provide dual-axis force sensing within the buckle body. The complete device measures $64\times 45\times 10\mathrm{mm}$ and weighs $34\mathrm{g}$, including its sensing and signal-processing electronics. Its force and angle ranges are 0–$200\mathrm{N}$ and $\pm 8°$, respectively. The average capacitance sensitivity was $1.33\mathrm{fF}/\mathrm{N}$, and the force linearity error was $6.96\%$ of full scale of output (FSO). Force root-mean-square error (RMSE) was $7.34\%$ of FSO without initial preload and $1.21$–$1.76\%$ of FSO with approximately $18\mathrm{N}$ preload in the respective tests. The validation angle RMSE was $1.67\%$ of FSO. Force hysteresis and repeatability were $8.88\%$ and $0.51\%$ of FSO, respectively. The force and angle FSO values were $200\mathrm{N}$ and $16°$. The estimated $-3\mathrm{dB}$ bandwidth was $2.7\mathrm{Hz}$. The proposed design provides a compact sensing interface for strap-based wearable robots.

Automating logistics tasks in factories has been challenging due to the high variability in processes. Among these tasks, robotic shelf picking is particularly challenging due to the limited capability of current robotic systems for reliable operation in unstructured environments. In particular, cluttered objects standing on shelves make it difficult to obtain object’s information, making it challenging to determine the suitable grasp candidate. This study proposes an integrated finger and retractable suction gripper to address the complexities of shelf picking and compensate for computer vision uncertainty. Specifically, the finger mechanism facilitates stable grasping by providing adaptive parallel support, even when object dimensions are not accurately estimated. The gripper achieves stable grasping even with a relatively simple vision model by employing a grasp-and-pull strategy following suction grasping. The system was tested in a simulated logistics environment, demonstrating its effectiveness in overcoming the lack of vision algorithms. This research contributes to developing a versatile robotic gripper for picking and stowing tasks on a shelf, essential for improving automation in the logistics industry.