Problem Statement
Navigating drones through dynamic environments poses challenges due to
obstacles, changing conditions, and the need for real-time decision-making. The
project addresses the development of an autonomous drone navigation system
that combines computer vision and machine learning to enable drones to
navigate through complex and dynamic environments safely and efficiently.
Abstract
The project focuses on enhancing the autonomy of drones by developing a
navigation system that utilizes computer vision for obstacle detection and
avoidance. Machine learning algorithms will be employed to enable the drone to
learn from its environment, adapting its navigation strategy based on real-time
data. The goal is to create a robust system that allows drones to operate
autonomously, making them suitable for various applications, including
surveillance, delivery, and inspection.
Outcome
● Implementation of a computer vision system for obstacle detection and
avoidance.
● Integration of machine learning algorithms for adaptive and autonomous
navigation.
● Enhanced drone autonomy, enabling safe and efficient navigation in
dynamic environments.
Reference
Autonomous robots are machines that can act without any human interference. When certain sensors and decision making algorithms are added to the control unit of a drone, the aerial vehicle is said to be autonomous. Such vehicles are capable of avoiding obstacles and correcting their local paths while staying on planned global paths. Autonomy in drones is usually achieved using many types of sensors like depth sensor, stereo camera or lidar, and intensive SLAM algorithms that require powerful processors. Though the existing methods work well, the scalability of such products is questionable as the economic and resource availability factors come into play. This paper proposes a method of achieving autonomous navigation using light weight embedded system and affordable monocular cameras by combining features of image processing and resource sharing. The proposed architecture makes use of monocular cues and midas depth estimation model to achieve obstacle avoidance and can run on any processor with basic features such as serial communication, wifi and camera ports.
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