Development of a low cost AI-capable drone for obstacle avoidance: a tutorial

As part of our research, we present a concept for an AI-enabled multi-rotor research aircraft for university research purposes. With a focus on cost efficiency without sacrificing performance, we have developed and tested the AI-enabled drone for monocular depth estimation (MTS) and obstacle avoidance in an in-door scenario under controlled test conditions (lighting conditions, take-off position and homogeneity of obstacles). A comprehensive GPS or IPS system is not required. This tutorial covers the essential aspects of developing and building camera drones, focusing on the integration of deep learning algorithms, sensor technologies and robust flight control systems. The MTS is limited to an in-door environment to prepare students for the research field of perception and provide opportunities for further development. An intuitive user interface in Python was developed as a basis. The proposed configuration is based for the first time on bidirectional communication between the Flight Control Computer (FCC) of the Beaglebone Blues (BBB) and the Flight Companion Computer (FCC) NVIDIA Orin Nano via a UART interface. The presented user interface was developed inhouse and is capable of adjusting the controller's parameters on-the-fly via a WiFi connection. This tutorial is intended to provide researchers, developers and students interested in developing flying robots for obstacle avoidance with insights into costeffective design strategies and the integration of AI technologies to improve the capabilities of drones.

Subjects

Low-Cost-UAV

Quadrocopter

Interface Analysis

Obstacle Avoidance

Monoclar-Depth-Estimation

DDC Class

629.13: Aviation Engineering

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Development of a low cost AI-capable drone for obstacle avoidance: a tutorial