Digital Compass Sensors Research Articles

Autonomous open-source electric wheelchair platform with internet-of-things and proportional-integral-derivative control

<span lang="EN-US">This study aims to improve the working model of autonomous wheelchair navigation for disabled patients using the internet of things (IoT). A proportional-integral-derivative (PID) control algorithm is applied to the autonomous wheelchair to control movement based on position coordinates and orientation provided by the global positioning system (GPS) and digital compass sensor. This system is controlled through the IoT system, which can be operated from a web browser. Autonomous wheelchairs are handled using a waypoint algorithm; ESP8266 is used as a microcontroller unit that acts as a bridge for transmitting data obtained by sensors and controlling the direct current (DC) motors as actuators. The proposed system and the autonomous wheelchair performance gave satisfactory results with a longitude and latitude error of 1.1 meters to 4.5 meters. This error is obtained because of the limitations of GPS with the type of Ublox Neo-M8N. As a starting point for further research, a mathematical model of a wheelchair was created, and pure pursuit control algorithm was used to simulate the movement. An open-source autonomous IoT platform for electric wheelchairs has been successfully created; this platform can help nurses and caretakers.</span>

DEVELOPMENT OF DIRECTIONAL ALGORITHM FOR THREE-WHEEL OMNIDIRECTIONAL AUTONOMOUS MOBILE ROBOT

A The proposed system developed an omnidirectional algorithm to control autonomous mobile robots with three wheels. The implementation system consists of three Planet DC motors with rated power of 80 W for three wheels, three encoders for speed feedback, one encoder for distance feedback, and one digital compass sensor for angle feedback. The main system with an STM32F407 microcontroller is designed for directional control of wheels based the signal received from compass sensor and encoder and then controls three subsystems to adjust the steering speed of each wheel. The sub-system is built to control only one DC motor for each wheel with the built-in proportional integral derivative controller (PID) algorithm by an STM32F103 microcontroller. Furthermore, the directional control algorithm is developed for three omnidirectional wheels and a PID algorithm is designed to control the speed of DC motor for each wheel. From the results the proposed system has the advantages: (1) to auto adjust the angle and position; (2) to erase the sensor for tracking line of the automobile robot; (3) cost-effectiveness and high accuracy

A novel GPS/DVL/MEMS-INS smartphone sensors integrated method to enhance autonomous navigation, guidance and control system of AUSVs based on ADSF Combined Filter

A reliable navigation, guidance and control system of Autonomous Unmanned Surface Vehicle (AUSV) is the major challenge for most researchers, especially when Global Positioning System (GPS) signal outages or its accuracy decreases in some places on the Earth’s surface. Our paper introduces a novel navigation, guidance and control system of AUSV as follows; the proposed navigation system of AUSV is based on integrated Micro Electric Mechanical System –Inertial Navigation System (MEMS-INS) smartphone sensors with GPS and Doppler Velocity Log (DVL) to correct AUSV navigation system errors. The guidance and control system of AUSV is based on Ultrasonic sensors, Raspberry Pi microcontroller, and digital compass sensor. The ultrasonic sensor is used to detect and avoid obstacles on the path of AUSV. The Raspberry Pi microcontroller and digital compass are used to guide AUSV in the required path.Since the GPS and DVL signals are used as the reference sources to correct errors of MEMS-INS system, the accuracy of GPS and DVL decreases due to a bad weather and due to associated noise of DVL measurement, which effects on a whole AUSV navigation system. To resolve this problem, the Adaptive Data Sharing Factor (ADSF) using Combined Filter (CF) is used as integrated method. According to proposed ADSF CF integrated method, the reference source (GPS or DVL) which has the beast accurate system will be used to correct the navigation errors. At same time the least accuracy system will detect and avoid.The efficiency of navigation, guidance and control system of AUSV based on proposed GPS/DVL/MEMS-INS ADFS CF integrated method is tested on surface reference trajectory with four obstacles located on the path of AUSV. During GPS outages, the AUSV was able to reach the target location to implement several tasks such as monitoring, exploring, tracking and returned back to base station depending on the stored path in its memory with high efficiency. Based on estimated results, the proposed navigation system could provide continuous navigation solution and reduce the navigation error to about 85.43% compared to GPS/DVL/MEMS CKF method and to about 68.14% compared to GPS/DVL/MEMS Constant DSF Combined Filter.