A navigation system for low-cost autonomous all-terrain-vehicles
1 online resource (88 pages) : PDF
University of North Carolina at Charlotte
In the last decade, there has been significant research work in the field of autonomous on-road vehicle, but, research in automating off-road vehicles has been largely untouched. The main aim of this thesis was to propose a robust framework for an off-road All-Terrain Vehicle (ATV) that can be used for navigation across all types of terrain. The ATV would navigate to the destination by following various way-points. This framework consists of a Global Positioning System (GPS) and an Inertial Measurement Unit (IMU). The GPS and Inertial Navigation System (INS) are two basic navigation systems. Due to their complementary characteristics in many aspects, a GPS/INS integrated navigation system is more accurate and dependable than having just either one of them. A sensor fusion was implemented for GPS and accelerometer to predict position and velocity using Kalman filtering. After the data was received from the Kalman filtering, and the main controller, i.e. the brain, could compare this data with the position of the GPS way-point and makes decisions regarding which direction the ATV was to be steered. All the actuators of the ATV are controlled by a micro-controller and the brain sends appropriate commands to the microcontroller controlling the actuators. This microcontroller then generates signals for either braking, changing the speed or moving the steering wheel depending on the actuator to which it is connected. All the sensors and actuator controllers are plug-and-play modules that are connected to a single Controller Area Network (CAN) bus, and thus can easily be removed, added, or upgraded. A library was built such that the controllers could only call Application Program Interface (API) functions, thus simplifying debugging the code, adding modularity to the program, and improving readability.
Electrical engineeringAutomobiles--Design and constructionComputer engineering
Api LibraryGPSINSKalman FilteringSensor Fusion
Tabkhi, HamedBrowne, Aidan
Thesis (M.S.)--University of North Carolina at Charlotte, 2019.
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