AI-Powered Obstacle Detection for Safer Human-Machine Collaboration

Abstract This article deals with ensuring and increasing the safety of mobile robotic systems in human-machine collaboration. The goal of the research was to design and implement an artificial intelligence application that recognizes obstacles, including humans, and increases safety. The resulting mobile Android application uses a MiDaS model to generate a depth map of the environment from the drone’s camera to approximate the distance from all obstacles to avoid the drone’s collision. Besides, this work introduced us to DJI Mobile SDK and neural network optimizations for their use on smartphones.

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Publication Details

Journal
Acta Electrotechnica et Informatica
Published
2024-09-01
DOI
https://doi.org/10.2478/aei-2024-0011
Citations
4
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
1.31

Funders

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article

AI-Powered Obstacle Detection for Safer Human-Machine Collaboration

Erik Kajáti, Iveta Zolotová, Maroš Krupáš, Mykyta Kot
4 citations
Acta Electrotechnica et Informatica
Robot Manipulation and Learning
1.31
article

AI-Powered Obstacle Detection for Safer Human-Machine Collaboration

Erik Kajáti, Iveta Zolotová, Maroš Krupáš, Mykyta Kot
article en
4 citations

Abstract

Abstract This article deals with ensuring and increasing the safety of mobile robotic systems in human-machine collaboration. The goal of the research was to design and implement an artificial intelligence application that recognizes obstacles, including humans, and increases safety. The resulting mobile Android application uses a MiDaS model to generate a depth map of the environment from the drone’s camera to approximate the distance from all obstacles to avoid the drone’s collision. Besides, this work introduced us to DJI Mobile SDK and neural network optimizations for their use on smartphones.

Acta Electrotechnica et InformaticaVol. 24(3)
Technical University of Košice (SK)
Kultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR
Openalex Percentile: Top 19%
Robot Manipulation and Learning
1.31
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