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Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System

The development of high-performance, low-cost unmanned aerial vehicles paired with rapid progress in vision-based perception systems herald a new era of autonomous flight systems with mission-ready capabilities. One of the key features of an autonomous UAV is a robust mid-air collision avoidance str...

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Autores principales: Park, Jeonghwan, Choi, Andrew Jaeyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385618/
https://www.ncbi.nlm.nih.gov/pubmed/37514592
http://dx.doi.org/10.3390/s23146297
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author Park, Jeonghwan
Choi, Andrew Jaeyong
author_facet Park, Jeonghwan
Choi, Andrew Jaeyong
author_sort Park, Jeonghwan
collection PubMed
description The development of high-performance, low-cost unmanned aerial vehicles paired with rapid progress in vision-based perception systems herald a new era of autonomous flight systems with mission-ready capabilities. One of the key features of an autonomous UAV is a robust mid-air collision avoidance strategy. This paper proposes a vision-based in-flight collision avoidance system based on background subtraction using an embedded computing system for unmanned aerial vehicles (UAVs). The pipeline of proposed in-flight collision avoidance system is as follows: (i) subtract dynamic background subtraction to remove it and to detect moving objects, (ii) denoise using morphology and binarization methods, (iii) cluster the moving objects and remove noise blobs, using Euclidean clustering, (iv) distinguish independent objects and track the movement using the Kalman filter, and (v) avoid collision, using the proposed decision-making techniques. This work focuses on the design and the demonstration of a vision-based fast-moving object detection and tracking system with decision-making capabilities to perform evasive maneuvers to replace a high-vision system such as event camera. The novelty of our method lies in the motion-compensating moving object detection framework, which accomplishes the task with background subtraction via a two-dimensional transformation approximation. Clustering and tracking algorithms process detection data to track independent objects, and stereo-camera-based distance estimation is conducted to estimate the three-dimensional trajectory, which is then used during decision-making procedures. The examination of the system is conducted with a test quadrotor UAV, and appropriate algorithm parameters for various requirements are deduced.
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spelling pubmed-103856182023-07-30 Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System Park, Jeonghwan Choi, Andrew Jaeyong Sensors (Basel) Article The development of high-performance, low-cost unmanned aerial vehicles paired with rapid progress in vision-based perception systems herald a new era of autonomous flight systems with mission-ready capabilities. One of the key features of an autonomous UAV is a robust mid-air collision avoidance strategy. This paper proposes a vision-based in-flight collision avoidance system based on background subtraction using an embedded computing system for unmanned aerial vehicles (UAVs). The pipeline of proposed in-flight collision avoidance system is as follows: (i) subtract dynamic background subtraction to remove it and to detect moving objects, (ii) denoise using morphology and binarization methods, (iii) cluster the moving objects and remove noise blobs, using Euclidean clustering, (iv) distinguish independent objects and track the movement using the Kalman filter, and (v) avoid collision, using the proposed decision-making techniques. This work focuses on the design and the demonstration of a vision-based fast-moving object detection and tracking system with decision-making capabilities to perform evasive maneuvers to replace a high-vision system such as event camera. The novelty of our method lies in the motion-compensating moving object detection framework, which accomplishes the task with background subtraction via a two-dimensional transformation approximation. Clustering and tracking algorithms process detection data to track independent objects, and stereo-camera-based distance estimation is conducted to estimate the three-dimensional trajectory, which is then used during decision-making procedures. The examination of the system is conducted with a test quadrotor UAV, and appropriate algorithm parameters for various requirements are deduced. MDPI 2023-07-11 /pmc/articles/PMC10385618/ /pubmed/37514592 http://dx.doi.org/10.3390/s23146297 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Park, Jeonghwan
Choi, Andrew Jaeyong
Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System
title Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System
title_full Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System
title_fullStr Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System
title_full_unstemmed Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System
title_short Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System
title_sort vision-based in-flight collision avoidance control based on background subtraction using embedded system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385618/
https://www.ncbi.nlm.nih.gov/pubmed/37514592
http://dx.doi.org/10.3390/s23146297
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AT choiandrewjaeyong visionbasedinflightcollisionavoidancecontrolbasedonbackgroundsubtractionusingembeddedsystem