Gyroscope Module Research Articles

Research on Internet of Things based Hand-Pushed Wheel Rangefinder Technology

In order to address the accuracy issues of the hand-pushed wheel rangefinder, this paper proposes a solution that integrates IoT technology and angle sensors into the device to enhance accuracy and informationization level. A wheel rangefinder based on Internet of Things (IoT) technology is designed. The central control device of this system adopts a STM32 microcontroller. The system includes a voltage boosting and dropping circuit, a reset circuit, a gyroscope module, an indicator light module, and a Wireless Fidelity (WiFi) transmission module. The gyroscope module is designed to calculate the actual running distance of the handcart through angle measurement, enabling real-time recording and remote transmission of measurement data. Compared with total stations, rangefinders, and measuring ropes, this system is of good effectiveness and its average error is within 1%. At the same time, it has the capability to save measurement information to the database.

A new artificial lateral line attitude perception method based on mixed activation function-multilayer perceptron (MAF-MLP)

Attitude perception is significant for state recognition and navigation of underwater applications. However, few studies have combined pressure sensors with gyroscopes for attitude sensing alone, and even fewer have addressed how to obtain the pitch angle, roll angle and travel speed at the same time. To make up for these shortcomings, an artificial lateral line attitude perception method based on mixed activation function-multilayer perceptron (MAF-MLP) model was proposed to recognize various attitudes of underwater vehicles. The artificial lateral line carrier model is created based on the lateral line structure of fish, and its surface pressure distribution characteristics are obtained by simulation analysis. The artificial lateral line (ALL) system and the data collection board equipped with a gyroscope module are used to conduct underwater experiments, and the gyroscope is used to collect the carrier attitude information in real-time to improve model prediction accuracy. Finally, the experimental data agree with the theoretical analysis results, and the MAF-MLP model is used as the data source to predict the carrier attitude. The results show that the MAF-MLP model can accurately predict the pitch angle, roll angle and travel speed, which provides a theoretical basis for realizing the state identification and navigation of the underwater vehicle.

Design and Development of Bio-Sensitive Robotic Arm using Gesture Control

In this paper, a model of a robotic arm is designed and manufactured using a 3D printer, which is to be operated via human gesture by using the Accelerometer and Gyroscope Module. This arm is proposed to help in the medical field, in military operations, in hazardous conditions and in industries to maximize human safety. The robot arm is designed such that, it has 5 degrees of freedom controlled by the 3-axis accelerometer mounted on the IC placed on the glove of the user. The module will replicate the movement of the user’s hand to extend, retract, and rotate accordingly to accurately position as required for the application. The user’s finger action is used to manipulate the working of the gripper. Therefore, gesture control in a broad sense is a computerized interface that allows computers to record and interpret those gestures into commands to adhere to actions.

Interactive Control using Bluetooth for Dual Axis Sun Tracker with Three Light Sensors

The use of renewable energy from solar panel systems is increasingly being applied, but until now the utilization has not been maximized. The movement of the sun caused by the rotation of the earth should be taken into account to maximize electrical energy in the solar panel (PV). In this study, a concept was proposed to control the movement of the two-axis sun tracker with smartphones using bluetooth communication. Controlled by rotating two DC motors, two axes are joined together into linear actuators that can extend and shorten, so that the solar panel can move towards the sun. This system uses a light sensor (Light Dependent Resistor) and a gyroscope sensor as input and two DC motors that are placed on each axis as output. The motor driver used is the Monster Moto Shield. Sun tracker that is used based on tetrahedron geometry and uses three light sensors. The light sensor functions to determine the position of the sun while the gyroscope sensor is used to determine the tilt angle of the solar panel. The gyroscope module used is MPU-6050. This system also uses the GY-273 compass sensor which functions to determine the direction of the wind direction. Components of input and output are connected to the ATmega328P microcontroller and wireless communication using bluetooth. The Bluetooth module used is the HC-05 module. The solar panel movement and the power obtained are then monitored and recorded for analysis

Construction and Control of the Bipedal Walking Robot

This article describes the design and testing of a walking robot. In the first stage, the mechanical behaviour of human’s lower limbs during the walk was observed and described to acquire data for the development of a simplified algorithm to control the legs of a walking robot. The second phase was the designing stage of the bipedal walking. Each robotic leg was equipped with six servo-drives. A gyroscope module with an accelerometer was used to measure the current position of the robot’s structure in space. The controller of a walking robot was developed and programmed based on Arduino Mega. The control algorithm stabilises the robot in an upright position. Potentiometers placed in the axes enabled measurements of angular positions of individual servos during the movement and were used to control walking. The programming of the movement is done through a smartphone which communicates with the robot's main controller using Bluetooth. Finally, the article describes the testing of the developed bipedal walking robot, documenting satisfactory results in walking.

Development of a self-balancing robot with a control moment gyroscope

This study introduces a two-wheeled self-balancing mobile robot based on a control moment gyroscope module. Two-wheeled mobile robots are able to achieve better mobility and rotation in small space...

Development of a 3D Pipe Robot for Smart Sensing and Inspection Using 3D Printing Technology

This 3D pipe inspection robot is capable of conducting fully autonomous pipe inspection, transmitting measured data to the monitoring end by wireless communication, drawing a 3D pipe routing map in real time, and detecting pipe anomalies. The robot is both lightweight and inexpensive because it is constructed by 3D printing. The main functions of the robot include calculating the robot posture using the measurements from the accelerometer and gyroscope module, drawing a 3D pipe routing map in real time, deciding the degree of contraction from pressure sensor module readings to detect abnormal expansion or contraction, and locating potential hazards in the pipe. The robot described in this paper is different from others in many ways. The freely extendable and contractible body structure allows the robot to easily adapt to pipes of various diameters. The wireless IP camera onboard transmits real-time videos and assists operators in looking for signs of damage or anomalies. This robot is practical and can be used for the inspection of damaged pipes, redrawing 3D pipe routing maps, and assisting in the regular maintenance of pipes underground or located within buildings, and may also be of use in rescue work and disasters, etc.

New Architecture and Configuration of Microelectromechanical Acceleration Measuring Gyro with Intermediate Bodies

The paper presents a new configuration of the gyroscope module comprising two sensors with antiphase primary vibrations and intermediate bodies on the axes of the informative secondary vibrations. This gyroscope module is a microelectromechanical acceleration measuring gyro which provides measurements of both angular velocities and linear accelerations.

Physical Activity Recognition based on Rotated Acceleration Data using Orientation Filter

PURPOSE: The purpose of the study was to examine the accuracy of physical activity (PA) classification algorithms using a rotational analysis. METHODS: 10 healthy, untrained males (age: 24.75±2.36, range 24-33yrs) participated in this study. Experimental protocol consisted of four stages: walking, running (horizontal movement: 75 meters), going up and down stairs (horizontal movement: twelve meters (m) and vertical movement: ten meters). A customized accelerometer and a gyroscope module were utilized to assess gait behaviors of participants. The devices were placed on a toe of each participant. Acceleration (i.e., x, y, and z) and gyroscope (i.e., yaw, pitch, and roll) data were recorded at 100Hz and transmitted to the customized android smartphone application (Galaxy Note II, Samsung). The data were analyzed as follows: 1) The modified Madgwick’s orientation filter was used to estimate the quaternion from acceleration and gyroscope module, 2) The acceleration data were rotated by quaternion in world fixed coordinate frame, 3) WEKA software was used to evaluate the patterns of PA (i.e., walking, running, going up and down stairs) from the rotated acceleration, 4) PA patterns (i.e. distance, speed, and direction of activities) were estimated from the post-processed rotational acceleration, depending on classified PA. RESULTS: 70% (1,266 steps) of the total data (1,758 steps) was applied to the machine-learning algorithm for training. The highest average accuracy of PA classification (99.84%) was observed using the Support Vector Machine (SVM) algorithm (Naïve Bayes: 94.53%, J48: 95.84%, and RBF Network: 98.54%). The confusion matrix showed over 92% accuracy (walking: 100%, running: 99.6%, going up stairs: 100%, going down stairs: 92.9%). The distance of walking and running was estimated as 96.22% (72.17±1.60 m) and 92.51% (69.38±4.15 m), respectively. The horizontal movement of going up and down stairs was estimated as 93.52% (11.22±0.71 m) and 94.77% (11.37±0.35 m), respectively. The vertical movement was estimated as 93.90% (9.39±0.36 m) and 94.38% (9.44±0.76 m), respectively. CONCLUSIONS: Results indicate that PA classification utilizing a rotational analysis provides an accurate prediction of PA patterns, including the average distance, speed, and direction of activities.