Far distance unmanned aerial vehicles control and object detection using internet of things network and embeded systems

Abstract

he new generation of Wireless Sensor Networks, that is known as the Internet of Things (IoT) enables the direct connection of physical objects to the Internet using microcontrollers. In most cases these microcontrollers have very limited computational resources. The global connectivity provides great opportunities for data collection and analysis as well as for interaction of objects that cannot be connected to the same local area network. The Internet of Things (IoT) is a technology that allows objects to be connected to the internet, enabling them with communication capabilities (with other objects and with people). By 2020 there will be over 50 billion things/objects connected to the internet, meaning this technology will completely revolutionize the world as we know it. IoT became an increasingly important research area around the world. Unmanned Aerial Vehicles (UAV) are becoming more common in our modern world. UAVs are mostly associated with war due to the coverage of their use in the recent wars in Iraq and Syria, but have the ability to do much more. UAVs are helpful tools in assessing damage after a disaster, keeping rescuers safe while they help those in need. UAVs are useful tools in monitoring crops to ensure the maximum yield is realized. The use of UAVs is also being used for monitoring remote land areas that are difficult to reach by foot. However, uses of UAVs are endless. This dissertation´s goal, is designing a system that uses internet of things (IoT) to make the control for UAV (drone) from an infant distance by simulating the signals coming xiii from RF receiver and remote control, these signals will be provided from Raspberry Pi3 and Arduino Uno devices to apply the signals of Roll, Pitch, Yaw, and Throttle to take control of UAV, the system uses an interaction between raspberry pi and flight controller to overcome the complexity of stability and PID control calculations. The control is designed using python 2.7 GUI (Graphical User Interface) to take control for flight, also the design is containing the capability of tracking objects based on the BGR color moment calculations.