Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Master of Science

Program

Computer Science

Supervisor

Anwar Haque

Abstract

Placing a number of home surveillance cameras around the property can enhance home security. However, camera coverage and their true effectiveness can be limited due to the limited number of cameras that can be installed, camera’s field of view, camera’s fixed position, and associated privacy issues. Unmanned aerial vehicles (UAVs), commonly known as drones, are able to fly independently without any human intervention. There are already a few commercially available options for outdoor drone surveillance, but none for indoor applications. We believe the drones can be effectively deployed for home monitoring purposes in a cost-effective and privacy-preserving manner. In this research, we developed a novel autonomous drone prototype that can offer economically viable effective smart home monitoring capabilities than currently available home monitoring solutions in today’s smart home industry. While in flight, our developed drone navigation system can fly on any predefined paths, dynamically change the paths based on user requirements to inspect any place within its range and adapt to unanticipated situations, such as obstacle avoidance and low battery. In addition, the system can utilize machine learning to evaluate the camera stream from the onboard camera and perform object detection tasks and notify users accordingly. In our testing, we demonstrated that our developed prototype successfully performed all the functions mentioned above. Also, our novel findings from this research shed light on some of the important parameters of indoor dronebased monitoring systems, which will contribute to the further advancement in drone-based home monitoring technology.

Summary for Lay Audience

A home surveillance system can increase the home's security by allowing the owner to view live footage from anywhere they desire. However, regular surveillance systems come at a cost where the system must be designed and implemented by trained professionals. Another issue with such a system is that the owner can only monitor certain sections of an area where cameras are installed. Nonetheless, some locations such as the basement, do not require regular monitoring, and installing a camera there is unnecessary. A quadrotor drone is one of the autonomous unmanned aerial vehicles (UAVs) that may fly with or without a human operator. A drone equipped with a camera can serve as a flying surveillance system, requiring no installation and can fly to any position on demand. When the drone system is armed, it can alert different drones to monitor different rooms or properties. If the user wishes to inspect a room where no drones are currently patrolling, the system can dispatch a drone to the desired spot and begin monitoring. In addition, sensors such as air quality and temperature sensors, can also be added to such drone systems in order to expand the system's capabilities.

In this study, we began by examining several types of drones and the components that allow for autonomous flight. Additionally, we analyzed various existing solutions for indoor and outdoor drone surveillance systems and crucial components for autonomous flight that other researchers have developed. By examining existing solutions, we identify potential challenges in indoor drone-based home monitoring. We present a system that enables multiple drones with forward-facing cameras to monitor home or business property environments. In addition, the solution can adapt to unforeseen circumstances, such as an obstruction in the flight path. We implemented the system on microdrones and tested it under various real-world conditions. In our testing, we demonstrated that the system could follow any predefined path, find a path in real-time to reach a new location asked by the user, find an alternate route when an unexpected obstacle is detected, and detect objects and provide notifications to its users.

Available for download on Sunday, February 23, 2025

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