Drone Obstacle Avoidance and Navigation Using Artificial Intelligence
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Journal Title
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Perustieteiden korkeakoulu |
Master's thesis
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Authors
Date
2018-05-14
Department
Major/Subject
Embedded System
Mcode
SCI3024
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
95 + 7
Series
Abstract
This thesis presents an implementation and integration of a robust obstacle avoidance and navigation module with ardupilot. It explores the problems in the current solution of obstacle avoidance and tries to mitigate it with a new design. With the recent innovation in artificial intelligence, it also explores opportunities to enable and improve the functionalities of obstacle avoidance and navigation using AI techniques. Understanding different types of sensors for both navigation and obstacle avoidance is required for the implementation of the design and a study of the same is presented as a background. A research on an autonomous car is done for better understanding autonomy and learning how it is solving the problem of obstacle avoidance and navigation. The implementation part of the thesis is focused on the design of a robust obstacle avoidance module and is tested with obstacle avoidance sensors such as Garmin lidar and Realsense r200. Image segmentation is used to verify the possibility of using the convolutional neural network for better understanding the nature of obstacles. Similarly, the end to end control with a single camera input using a deep neural network is used for verifying the possibility of using AI for navigation. In the end, a robust obstacle avoidance library is developed and tested both in the simulator and real drone. Image segmentation is implemented, deployed and tested. A possibility of an end to end control is also verified by obtaining a proof of concept.Description
Supervisor
Jung, AlexThesis advisor
Ramirez, EnriqueKeywords
drones, obstacle avoidance, autonomous navigation, artificial intelligence, computer vision, deep neural network