LAUSR

Abstract 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.