Development of a low-cost navigation system for autonomous off-road vehicles

The main objective of this dissertation is to develop a low-cost navigation system with sufficient accuracy, integrity, and robustness for use in autonomous off-road vehicles. This system consists of a global positioning system (GPS) and an integrated inertial sensor (IIS) unit. It can achieve positioning accuracy in the range of sub-meter with cost of {dollar}600–{dollar}1,400 at the current market price. The major challenge of developing this low-cost navigation system is finding how to efficiently integrate the outputs from the GPS and IIS in order to extract an accurate positioning solution.; At first, a low-cost IIS, consisting of three MEMS (micro-electro-mechanical systems) gyros and a MEMS triaxial accelerometer, is developed to measure vehicle attitude. This IIS is then evaluated on a vehicle using a highly accurate inertial measurement unit (IMU). To effectively integrate the data from the GPS and IIS, a Kalman filter, based on a position-velocity-attitude (PVA) model of the vehicle, is developed for the GPS/IIS fusion to reduce the positioning error, increase the update rate of the fusion system, and bridge the GPS signal outage. With this fusion algorithm, the navigation system can accurately estimate vehicle position based on the redundant data from various sensors. In addition, in order to reduce the computational intensity of the Kalman filter, this dissertation also proposes a different GPS/IIS fusion algorithm, which is based on an IIS error model and a complementary Kalman filter.; To evaluate this low-cost navigation system and associated fusion algorithms, validation tests are performed using an experimental system that is designed to acquire real-time data and to perform data fusion. These tests take place at three test sites that include flat and uneven terrain, with or without obstacles. The evaluation results show that this system can achieve positioning accuracy in the range of 0.1 m to 0.5 m with an update rate of 50 Hz. The PVA model-based fusion algorithm can effectively bridge the signal interruption during a GPS signal outage of 30 s. The error model-based fusion algorithm can provide the signals at 50 Hz using the 1 Hz update rate of the complementary Kalman filter.