Autonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm

Abstract This paper presents an autonomous navigation system for cost-effective magnetic-assisted colonoscopy, employing force-based sensors, an actuator, a proportional–integrator controller and a real-time heuristic searching method. The force sensing system uses load cells installed between the r...

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Autores principales:	Hao-En Huang, Sheng-Yang Yen, Chia-Feng Chu, Fat-Moon Suk, Gi-Shih Lien, Chih-Wen Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/74572c898ec840fb8f6da0b4b13cc6bc
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spelling	oai:doaj.org-article:74572c898ec840fb8f6da0b4b13cc6bc2021-12-02T19:06:38ZAutonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm10.1038/s41598-021-95760-72045-2322https://doaj.org/article/74572c898ec840fb8f6da0b4b13cc6bc2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95760-7https://doaj.org/toc/2045-2322Abstract This paper presents an autonomous navigation system for cost-effective magnetic-assisted colonoscopy, employing force-based sensors, an actuator, a proportional–integrator controller and a real-time heuristic searching method. The force sensing system uses load cells installed between the robotic arm and external permanent magnets to derive attractive force data as the basis for real-time surgical safety monitoring and tracking information to navigate the disposable magnetic colonoscope. The average tracking accuracy on magnetic field navigator (MFN) platform in x-axis and y-axis are 1.14 ± 0.59 mm and 1.61 ± 0.45 mm, respectively, presented in mean error ± standard deviation. The average detectable radius of the tracking system is 15 cm. Three simulations of path planning algorithms are presented and the learning real-time A* (LRTA) algorithm with our proposed directional heuristic evaluation design has the best performance. It takes 75 steps to complete the traveling in unknown synthetic colon map. By integrating the force-based sensing technology and LRTA path planning algorithm, the average time required to complete autonomous navigation of a highly realistic colonoscopy training model on the MFN platform is 15 min 38 s and the intubation rate is 83.33%. All autonomous navigation experiments are completed without intervention by the operator.Hao-En HuangSheng-Yang YenChia-Feng ChuFat-Moon SukGi-Shih LienChih-Wen LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hao-En Huang Sheng-Yang Yen Chia-Feng Chu Fat-Moon Suk Gi-Shih Lien Chih-Wen Liu Autonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm
description	Abstract This paper presents an autonomous navigation system for cost-effective magnetic-assisted colonoscopy, employing force-based sensors, an actuator, a proportional–integrator controller and a real-time heuristic searching method. The force sensing system uses load cells installed between the robotic arm and external permanent magnets to derive attractive force data as the basis for real-time surgical safety monitoring and tracking information to navigate the disposable magnetic colonoscope. The average tracking accuracy on magnetic field navigator (MFN) platform in x-axis and y-axis are 1.14 ± 0.59 mm and 1.61 ± 0.45 mm, respectively, presented in mean error ± standard deviation. The average detectable radius of the tracking system is 15 cm. Three simulations of path planning algorithms are presented and the learning real-time A* (LRTA) algorithm with our proposed directional heuristic evaluation design has the best performance. It takes 75 steps to complete the traveling in unknown synthetic colon map. By integrating the force-based sensing technology and LRTA path planning algorithm, the average time required to complete autonomous navigation of a highly realistic colonoscopy training model on the MFN platform is 15 min 38 s and the intubation rate is 83.33%. All autonomous navigation experiments are completed without intervention by the operator.
format	article
author	Hao-En Huang Sheng-Yang Yen Chia-Feng Chu Fat-Moon Suk Gi-Shih Lien Chih-Wen Liu
author_facet	Hao-En Huang Sheng-Yang Yen Chia-Feng Chu Fat-Moon Suk Gi-Shih Lien Chih-Wen Liu
author_sort	Hao-En Huang
title	Autonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm
title_short	Autonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm
title_full	Autonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm
title_fullStr	Autonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm
title_full_unstemmed	Autonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm
title_sort	autonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/74572c898ec840fb8f6da0b4b13cc6bc
work_keys_str_mv	AT haoenhuang autonomousnavigationofamagneticcolonoscopeusingforcesensingandaheuristicsearchalgorithm AT shengyangyen autonomousnavigationofamagneticcolonoscopeusingforcesensingandaheuristicsearchalgorithm AT chiafengchu autonomousnavigationofamagneticcolonoscopeusingforcesensingandaheuristicsearchalgorithm AT fatmoonsuk autonomousnavigationofamagneticcolonoscopeusingforcesensingandaheuristicsearchalgorithm AT gishihlien autonomousnavigationofamagneticcolonoscopeusingforcesensingandaheuristicsearchalgorithm AT chihwenliu autonomousnavigationofamagneticcolonoscopeusingforcesensingandaheuristicsearchalgorithm
_version_	1718377158716424192

Autonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm

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