Development of Integrative Methodologies for Effective Excavation Progress Monitoring
Excavation is one of the primary projects in the construction industry. Introducing various technologies for full automation of the excavation can be a solution to improve sensing and productivity that are the ongoing issues in this area. This paper covers three aspects of effective excavation progr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/2/364 |
| _version_ | 1797415093663694848 |
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| author | Abdullah Rasul Jaho Seo Amir Khajepour |
| author_facet | Abdullah Rasul Jaho Seo Amir Khajepour |
| author_sort | Abdullah Rasul |
| collection | DOAJ |
| description | Excavation is one of the primary projects in the construction industry. Introducing various technologies for full automation of the excavation can be a solution to improve sensing and productivity that are the ongoing issues in this area. This paper covers three aspects of effective excavation progress monitoring that include excavation volume estimation, occlusion area detection, and 5D mapping. The excavation volume estimation component enables estimating the bucket volume and ground excavation volume. To achieve mapping of the hidden or occluded ground areas, integration of proprioceptive and exteroceptive sensing data was adopted. Finally, we proposed the idea of 5D mapping that provides the info of the excavated ground in terms of geometric space and material type/properties using a 3D ground map with LiDAR intensity and a ground resistive index. Through experimental validations with a mini excavator, the accuracy of the two different volume estimation methods was compared. Finally, a reconstructed map for occlusion areas and a 5D map were created using the bucket tip’s trajectory and multiple sensory data with convolutional neural network techniques, respectively. The created 5D map would allow for the provision of extended ground information beyond a normal 3D ground map, which is indispensable to progress monitoring and control of autonomous excavation. |
| first_indexed | 2024-03-09T05:43:04Z |
| format | Article |
| id | doaj.art-52f54319c1d040849050416e3b5e289a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:43:04Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-52f54319c1d040849050416e3b5e289a2023-12-03T12:23:16ZengMDPI AGSensors1424-82202021-01-0121236410.3390/s21020364Development of Integrative Methodologies for Effective Excavation Progress MonitoringAbdullah Rasul0Jaho Seo1Amir Khajepour2Department of Automotive and Mechatronics Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaDepartment of Automotive and Mechatronics Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaDepartment of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaExcavation is one of the primary projects in the construction industry. Introducing various technologies for full automation of the excavation can be a solution to improve sensing and productivity that are the ongoing issues in this area. This paper covers three aspects of effective excavation progress monitoring that include excavation volume estimation, occlusion area detection, and 5D mapping. The excavation volume estimation component enables estimating the bucket volume and ground excavation volume. To achieve mapping of the hidden or occluded ground areas, integration of proprioceptive and exteroceptive sensing data was adopted. Finally, we proposed the idea of 5D mapping that provides the info of the excavated ground in terms of geometric space and material type/properties using a 3D ground map with LiDAR intensity and a ground resistive index. Through experimental validations with a mini excavator, the accuracy of the two different volume estimation methods was compared. Finally, a reconstructed map for occlusion areas and a 5D map were created using the bucket tip’s trajectory and multiple sensory data with convolutional neural network techniques, respectively. The created 5D map would allow for the provision of extended ground information beyond a normal 3D ground map, which is indispensable to progress monitoring and control of autonomous excavation.https://www.mdpi.com/1424-8220/21/2/364excavation progressground volume estimationbucket volume estimationocclusion areaproprioceptive and exteroceptive sensors5D mapping |
| spellingShingle | Abdullah Rasul Jaho Seo Amir Khajepour Development of Integrative Methodologies for Effective Excavation Progress Monitoring Sensors excavation progress ground volume estimation bucket volume estimation occlusion area proprioceptive and exteroceptive sensors 5D mapping |
| title | Development of Integrative Methodologies for Effective Excavation Progress Monitoring |
| title_full | Development of Integrative Methodologies for Effective Excavation Progress Monitoring |
| title_fullStr | Development of Integrative Methodologies for Effective Excavation Progress Monitoring |
| title_full_unstemmed | Development of Integrative Methodologies for Effective Excavation Progress Monitoring |
| title_short | Development of Integrative Methodologies for Effective Excavation Progress Monitoring |
| title_sort | development of integrative methodologies for effective excavation progress monitoring |
| topic | excavation progress ground volume estimation bucket volume estimation occlusion area proprioceptive and exteroceptive sensors 5D mapping |
| url | https://www.mdpi.com/1424-8220/21/2/364 |
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