Multidirectional Cylindrical Piezoelectric Force Sensor: Design and Experimental Validation

A common design concept of the piezoelectric force sensor, which is to assemble a bump structure from a flat or fine columnar piezoelectric structure or to use a specific type of electrode, is quite limited. In this paper, we propose a new design of cylindrical piezoelectric sensors that can detect multidirectional forces. The proposed sensor consists of four row and four column sensors. The design of the sensor was investigated by the finite element method. The response of the sensor to various force directions was observed, and it was demonstrated that the direction of the force applied to the sensor could be derived from the signals of one row sensor and three column sensors. As a result, this sensor proved to be able to detect forces in the area of 225° about the central axis of the sensor. In addition, a cylindrical sensor was fabricated to verify the proposed sensor and a series of experiments were performed. The simulation and experimental results were compared, and the actual sensor response tended to be similar to the simulation

Flexible Piezoelectric Sensor-Based Gait Recognition

Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %

Decay characteristics of electroadhesive forces by periodic electrodes in dielectric layers

Electroadhesive force is the force generated by induced dipoles in the gradient of an electric field. Owing to its benefits of mechanical characteristics and versatility, it is widely used to hold and manipulate objects in robotic applications. So far, most studies in this field have been focused on the maximization of the magnitude of electroadhesive force. In this paper, we focus on the decay characteristics of electroadhesive force depending on the spatial distance from electrodes to employ the force to precisely separate a single layer from stacked dielectric layers. It turns out that all configurations with periodically repeating electrodes’ arrangement, have the same decay characteristics which significantly depend on the geometrical period of the electrode patterns. Also, we find that the other parameters including the applied voltage and geometry of electrodes have little effect on the decay characteristics. The electric potential of an arbitrary electrode configuration is expanded in terms of the Fourier series, and we use it to analytically prove the high dependence of decay characteristics on the geometrical period. Numerical analysis is performed using the finite element method

Fe3O4–Silicone Mixture as Flexible Actuator

In this study, we introduce Fe3O4-silicone flexible composite actuators fabricated by combining silicone and iron oxide particles. The actuators exploit the flexibility of silicone and the electric conductivity of iron oxide particles. These actuators are activated by electrostatic force using the properties of the metal particles. Herein, we investigate the characteristic changes in actuation performance by increasing the concentration of iron oxide from 1% to 20%. The developed flexible actuators exhibit a resonant frequency near 3 Hz and their actuation amplitudes increase with increasing input voltage. We found that the actuator can move well at metal particle concentrations >2.5%. We also studied the changes in actuation behavior, depending on the portion of the Fe3O4-silicone in the length. Overall, we experimentally analyzed the characteristics of the newly proposed metal particle-silicone composite actuators

Chopstick Robot Driven by X-shaped Soft Actuator

Chopsticks are a popular tool used every day by 1.5 billion people to pick up pieces of food of different sizes and shapes. Given that the use of chopsticks requires sophisticated muscle control, they are difficult to use for unskilled people. In this study, a chopstick robot that uses a new soft actuator was developed. Firstly, we developed an X-shaped soft actuator and tested its performance. When a voltage was applied to the actuator, the gap in the X shape was reduced by the resulting electrostatic force. Conversely, when the power was turned off, the actuator recovered its original shape owing to the elasticity of its material. We attached the X-shaped soft actuator between the chopsticks. The chopstick robot, controlled by the input voltage, can pick up various objects in the switched-on state and is able to release them when switched off. We tested the performance of the chopstick robot and analyzed the forces acting on the chopsticks. The robot can be used for picking up various objects. Moreover, the X-shaped actuator can be adapted for use in various studies, through different shapes and configurations

A V-Shaped Actuator Utilizing Electrostatic Force

In this study, we propose a new ‘V’-shaped actuator with two panels and experimentally and theoretically investigate its actuation to find the most efficient structure. The V-shaped actuator operates like a seesaw. Specifically, when a high voltage input is applied between the V-shaped actuator and metal plate at the bottom substrate, another panel rises due to electrostatic attraction. Both gravity and electrostatic attraction forces are utilized for the operation of the actuator. We made a model of the actuation mechanism considering torque, gravity, and electrostatic forces. Theoretical values were compared with experimental results considering all factors of force applied to actuators. Additionally, we added torque by restoring force to compensate for the experimental conditions. The theoretical value almost coincided with the experimental value with R2 = 0.9

Flexible Piezoelectric Sensor-Based Gait Recognition

Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %

Patient Posture Monitoring System Based on Flexible Sensors

Monitoring patients using vision cameras can cause privacy intrusion problems. In this paper, we propose a patient position monitoring system based on a patient cloth with unobtrusive sensors. We use flexible sensors based on polyvinylidene fluoride, which is a flexible piezoelectric material. Theflexiblesensorsareinsertedintopartsclosetothekneeandhipoftheloosepatientcloth. We measure electrical signals from the sensors caused by the piezoelectric effect when the knee and hip in the cloth are bent. The measured sensor outputs are transferred to a computer via Bluetooth. We use a custom-made program to detect the position of the patient through a rule-based algorithm and the sensor outputs. The detectable postures are based on six human motions in and around a bed. The proposed system can detect the patient positions with a success rate over 88 percent for three patients

Electro-hydraulic actuator for a soft gripper

There is a considerable demand to develop robots that can perform sophisticated tasks such as grabbing delicate materials, passing through narrow pathways, and acting as mediators between humans and robots. Soft robots can provide a solution for such applications. In this study, we propose an electrohydraulic gripper, which is based on electrostatic and hydraulic forces. Interestingly, the gripper generates a hydraulic force without an external fluid supply source. In addition, it achieves good compliance, because the gripper is composed of soft materials such as polyethylene film and silicone. We experimentally investigate the characteristics of the actuator of the gripper. In addition, the electrohydraulic gripper demonstrates an ability to grasp delicate materials. © Copyright 2020, Mary Ann Liebert, Inc., publishers 2020.11Nsciescopu

Classification of Floor Materials Using Piezoelectric Actuator–Sensor Pair and Deep Learning for Mobile Robots

Analyzing floor surface materials is critical for controlling the motion and tasks of mobile robots. In this study, we propose a novel method for classifying floor materials for indoor mobile robots using a piezoelectric actuator–sensor pair and deep learning. This method can classify the floor properties itself with isolated sensing system while the mobile robot is moving. The piezoelectric pair is a thin-film type. It consists of an actuator and a sensor. The sensing pair is positioned at the bottom of the robot. When the robot moves forward, the sensing part collects the electrical responses from the actuator. Since one-dimensional data is collected through the piezoelectric actuator-sensor pair, the size of the system is small and the data processing speed can be reduced. Using this mechanism, experiments were conducted to classify various materials of floor surfaces in indoor environments. The sensing data were processed by fast Fourier transform, high-pass filter, polynomial fitting, and sampling to be used as inputs for machine learning of the classification model. Specifically, the trained model achieved a high accuracy of 95.4%. In addition, the training data were verified using the k-means clustering method. Moreover, the effect of the physical properties on the sensor data was analyzed to investigate the relationship between the materials and the sensing outputs