Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework

Human visual inspection of drains is laborious, time-consuming, and prone to accidents. This work presents an AI-enabled robot-assisted remote drain inspection and mapping framework using our in-house developed reconfigurable robot Raptor. The four-layer IoRT serves as a bridge between the users and...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Povendhan Palanisamy, Rajesh Elara Mohan, Archana Semwal, Lee Ming Jun Melivin, Braulio Félix Gómez, Selvasundari Balakrishnan, Karthikeyan Elangovan, Balakrishnan Ramalingam, Dylan Ng Terntzer
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	reconfigurable robot defect inspection drain inspection deep learning computer vision mapping Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/3f3d9326ccbf421580bce51ec7902033
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3f3d9326ccbf421580bce51ec7902033
record_format	dspace
spelling	oai:doaj.org-article:3f3d9326ccbf421580bce51ec79020332021-11-11T19:14:30ZDrain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework10.3390/s212172871424-8220https://doaj.org/article/3f3d9326ccbf421580bce51ec79020332021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7287https://doaj.org/toc/1424-8220Human visual inspection of drains is laborious, time-consuming, and prone to accidents. This work presents an AI-enabled robot-assisted remote drain inspection and mapping framework using our in-house developed reconfigurable robot Raptor. The four-layer IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The Faster RCNN ResNet50, Faster RCNN ResNet101, and Faster RCNN Inception-ResNet-v2 deep learning frameworks were trained using a transfer learning scheme with six typical concrete defect classes and deployed in an IoRT framework remote defect detection task. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trials using the SLAM technique. The experimental results indicate that robot’s maneuverability was stable, and its mapping and localization were also accurate in different drain types. Finally, for effective drain maintenance, the SLAM-based defect map was generated by fusing defect detection results in the lidar-SLAM map.Povendhan PalanisamyRajesh Elara MohanArchana SemwalLee Ming Jun MelivinBraulio Félix GómezSelvasundari BalakrishnanKarthikeyan ElangovanBalakrishnan RamalingamDylan Ng TerntzerMDPI AGarticlereconfigurable robotdefect inspectiondrain inspectiondeep learningcomputer visionmappingChemical technologyTP1-1185ENSensors, Vol 21, Iss 7287, p 7287 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	reconfigurable robot defect inspection drain inspection deep learning computer vision mapping Chemical technology TP1-1185
spellingShingle	reconfigurable robot defect inspection drain inspection deep learning computer vision mapping Chemical technology TP1-1185 Povendhan Palanisamy Rajesh Elara Mohan Archana Semwal Lee Ming Jun Melivin Braulio Félix Gómez Selvasundari Balakrishnan Karthikeyan Elangovan Balakrishnan Ramalingam Dylan Ng Terntzer Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework
description	Human visual inspection of drains is laborious, time-consuming, and prone to accidents. This work presents an AI-enabled robot-assisted remote drain inspection and mapping framework using our in-house developed reconfigurable robot Raptor. The four-layer IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The Faster RCNN ResNet50, Faster RCNN ResNet101, and Faster RCNN Inception-ResNet-v2 deep learning frameworks were trained using a transfer learning scheme with six typical concrete defect classes and deployed in an IoRT framework remote defect detection task. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trials using the SLAM technique. The experimental results indicate that robot’s maneuverability was stable, and its mapping and localization were also accurate in different drain types. Finally, for effective drain maintenance, the SLAM-based defect map was generated by fusing defect detection results in the lidar-SLAM map.
format	article
author	Povendhan Palanisamy Rajesh Elara Mohan Archana Semwal Lee Ming Jun Melivin Braulio Félix Gómez Selvasundari Balakrishnan Karthikeyan Elangovan Balakrishnan Ramalingam Dylan Ng Terntzer
author_facet	Povendhan Palanisamy Rajesh Elara Mohan Archana Semwal Lee Ming Jun Melivin Braulio Félix Gómez Selvasundari Balakrishnan Karthikeyan Elangovan Balakrishnan Ramalingam Dylan Ng Terntzer
author_sort	Povendhan Palanisamy
title	Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework
title_short	Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework
title_full	Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework
title_fullStr	Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework
title_full_unstemmed	Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework
title_sort	drain structural defect detection and mapping using ai-enabled reconfigurable robot raptor and iort framework
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3f3d9326ccbf421580bce51ec7902033
work_keys_str_mv	AT povendhanpalanisamy drainstructuraldefectdetectionandmappingusingaienabledreconfigurablerobotraptorandiortframework AT rajeshelaramohan drainstructuraldefectdetectionandmappingusingaienabledreconfigurablerobotraptorandiortframework AT archanasemwal drainstructuraldefectdetectionandmappingusingaienabledreconfigurablerobotraptorandiortframework AT leemingjunmelivin drainstructuraldefectdetectionandmappingusingaienabledreconfigurablerobotraptorandiortframework AT brauliofelixgomez drainstructuraldefectdetectionandmappingusingaienabledreconfigurablerobotraptorandiortframework AT selvasundaribalakrishnan drainstructuraldefectdetectionandmappingusingaienabledreconfigurablerobotraptorandiortframework AT karthikeyanelangovan drainstructuraldefectdetectionandmappingusingaienabledreconfigurablerobotraptorandiortframework AT balakrishnanramalingam drainstructuraldefectdetectionandmappingusingaienabledreconfigurablerobotraptorandiortframework AT dylanngterntzer drainstructuraldefectdetectionandmappingusingaienabledreconfigurablerobotraptorandiortframework
_version_	1718431578953089024

Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework

Ejemplares similares