Empirical Development of 3D Terrain Processing Platform for Autonomous Excavation System

Transcription

1 Empirical Development of 3D Terrain Processing Platform for Autonomous Excavation System Seok Kim 1, Tae-yeong Kim 2, Soonwook Kwon 3, Jongwon Seo 4 1 Senior Researcher, Korean Institute of Civil Engineering and Building Technology, Goyang, South Korea (Corresponding Author: kimseok@kict.re.kr) 2 Researcher, Korean Institute of Civil Engineering and Building Technology, Goyang, South Korea 3 Professor, School of Civil, Architectural, and Environmental Engineering, SungKyunKwan University, Suwon, South Korea 4 Professor, Dept. of Civil and Environmental Engineering, Hanyang University, Seoul, South Korea ABSTRACT: With the advancement in measurement technology, the earthwork site can be scanned within a short period of time using 3D terrestrial laser scanning (TLS). While companies want to digitize an earthwork site for different purposes, they all ultimately want to achieve automated earthwork including earthwork planning and calculation of earthwork volume and operation of earthwork equipment. In this study, the 3D terrain processing platform was defined and developed as a prototype. The 3D terrain processing platform suggested in this study was developed as prototype software. It was also verified with simply scanned data and design drawings. In the verification of the DB using the 3D terrain processing platform, it was found that each analysis cell had the required information including coordinates, soil qualities, boring information, earthwork volume, work type (cutting or filling), etc. INTRODUCTION Earthwork is one of the essential construction works done to construct a building or a structure on the ground. Earthwork is a traditional work type, in which the work site is measured using a total station based on which an earthwork plan is provided. According to the plan, earthwork is executed to complete the entire work by using construction equipment such as bulldozer, backhoe loader, fork-crane, and truck. Since the mid-1990s, many studies have been conducted on intelligent excavation system to achieve automated earthwork. Here, an intelligent excavation system is a

2 technology to improve construction quality and productivity through the effective management of information technology and construction equipment [Singh, 1997]. In 2000, the initial form of an intelligent excavation system for automated earthwork was introduced as a construction machine equipped with a guidance system. In recent years, many studies have been conducted with the aim of developing a more advanced type of semi-automated or automated machine. The research findings of many studies on automated earthwork discuss how an earthwork site can be digitized, analyzed and utilized. As earthwork sites have too many uncertain factors to allow them to be digitized, many researchers have made efforts to solve this problem. Photogrammetry and the 3D terrestrial laser scanning system are the most notable methods. Rapid advancements are being made in these technologies, and many methodologies have been studied to digitize an earthwork site by using them. In particular, the 3D terrestrial scanning system is in wide use in many construction sites due to the accuracy of its measurements regardless of the source of light. For example, it is applied to many fields including calculation of earthwork [Du and Teng 2007, Slattery et al. 2012], deformation measurement of topography [Hashash et al. 2005, Hashash and Finno 2008, Prokop and Panholzer 2009], inspection of tunnel shape [Pejic 2013, Park et al. 2012, Nutterns et al. 2010], and deformation measurement of dam [Park et al. 2009]. It has also recently been used for 3D imaging of an earthwork site in order to provide fundamental data for automated control and guidance for heavy equipment such as fork-crane and bulldozer [Chae et al. 2009, Chae et al. 2011]. The purposes of digitizing an earthwork site can differ, but the ultimate purpose is to automate earthwork-related tasks such as establishing an earthwork plan, calculating the earthwork volume and operating earthwork equipment through the digitized earthwork site. Here, we should note that it is hard to consider an earthwork site as digitized even though the earthwork site has been scanned using a 3D scanner. As pointed out above, for the automation of earthwork, it should be possible to plan an earthwork plan, calculate earthwork volume and operate earthwork equipment; however, it is impossible to perform all of these functions with the scanned data alone. In this study we defined the 3D terrain processing platform required to realize automated earthwork based on the topographic function of the 3D terrestrial scanner, and developed it as a prototype. RESEARCH SCOPE AND METHODOLOGY The research scope and methodology for this study was set as described below to meet the following conditions and present a 3D terrain processing platform. 1 The interface function of the scanned data was not taken into account. If the earthwork site is scanned from a location, blind spots the laser scanner cannot capture cause distortion. For this reason, the data scanned from several locations are registered in order to increase the accuracy. However, the registering algorithm is not considered separately. Under the hypothesis that the scanned data is well registered, the data of point clouds are used.

3 2 The ground information is updated in consideration of the boring information and soil properties only. Information required for the earthwork may vary depending on the characteristics of the construction project or the construction area. However, only boring information and soil qualities commonly required for earthwork are considered in this study to update the ground property information. 3 The basic unit of this analysis is set as an analysis cell. To make it possible to make an earthwork plan, calculate earthwork volume and operate earthwork equipment, it is necessary to define the basic unit of information required for automated earthwork. The 3D scanned data cannot have volume itself because they are point clouds; therefore, they are not appropriate for an earthwork plan or the calculation of earthwork volume. Moreover, if they are entered for the entire space information after being converted into solid information, the practical analysis may take too much time because the data is too big. Therefore, an appropriate size of analysis cell is set as a basic unit to carry out a spatial analysis. IMPLEMENTATION OF 3D TERRAIN PROCESSING PLATFORM The 3D Terrain Processing Platform consists of 'updating soil properties, creating analysis cells and sending data to the integrated system. Of these, the updating soil properties and the creating analysis cells play the role of providing the analysis cells on which the basic properties are entered for the integrated system. Figure 1 illustrates how to process the data with the modules

4 FIG. 1. Process of the data in the integrated system 1. Loading of scanned data and plan drawing There are many noise points in the 3D point clouds collected from the 3D laser scanner at the site due to factors in the earthwork environment, such as trees and grass. Before soil properties are updated, the data is processed using a noise reduction function. Next, the plan drawing designed using software such as CAD is projected on the point cloud and then placed accurately by setting it with relative and absolute coordinates. FIG. 2. Loading of the scanned data & plan drawing

5 2. Updating Soil Properties Each point in the 3D point cloud has the information of X, Y, Z, RGB, and intensity. The module to update soil properties has the function of providing the geotechnical information needed to prepare an earthwork plan. The additional geotechnical information includes soil quality obtained through a boring investigation, state function, relative density, groundwater level, soil volume conversion factor, and weight of unit volume as calculated in the field density test, as shown in Table 1. Division Table 1. Additional geotechnical information Content Information from boring investigation Information from soil property test strata, altitude, groundwater level, relative density, state function Soil volume conversion factor (L, C), weight of unit volume 2.1 Update of the data from the boring investigation With respect to the data collected from the boring investigation, a user puts a serial number and the coordinates of each location on which the boring investigation was conducted. The boring investigation is usually done from the surface of the earth to the rock formation, which may be composed of many strata. For this reason, in this study, the strata were separated, and it was designed to enter the information such as altitude, stratum, density and dampness for each stratum. Once all the information was entered, it was interpolated in consideration of the earth s true surface to reflect the soil properties on all areas within the boundary as illustrated in Figure 3. FIG. 3. Boring investigation and soil properties information after the interpolation 2.2 Updating the data from the soil property test

6 On the other hand, the data obtained though the field density test was numbered and coordinates and altitude were then entered, and then the data from the field density test was entered for each location. Unlike the boring investigation, the soil density is tested in a test pit on the surface of the earth; for this reason, the depth from the surface was entered as appropriate for the test conditions, and then soil volume conversion factor (C,L) and weight of unit volume were input. Like the data from the boring investigation, it was also interpolated to reflect soil properties on the entire area within the boundary(figure 3). 3. Creating Analysis Cells The Creating Analysis Cells module sets the range of the boundary, and divides it into basic cell units. Next, the cell type is assigned and the data is entered. 3.1 Setting the range of the Cell The Cell analysis range was set in a cube in the center of the boundaries of the drawing projected on the point cloud. Here, the cube form is set for an index for Octree. Also, the unit length is set to the maximum distance in the X-, Y- and Z- directions in order to include all the boundaries, as shown in Equations 1 and Figure 4. Unit length of range = Max(X, Y, Z) (1) (a)x-, Y- directions FIG. 4. Cell range (b) Z- direction 3.2 Dividing a Cell Once the analysis range is set, it is divided into basic cell units; at this, the cells are systemically divided using the Octree algorithm. Octree is a conceptual extension from quadtree for 2D space, and it is the most efficient method for point cloud indexing. Octree algorithm is used to divide a cube-formed space (parent node) into eight identical sub-cubes (child node), each of which is assigned an identification number, and is also utilized in diverse studies related with 3D point cloud processing (Saxena et al., 1995; Marcchal, 2009). Octree repeatedly partitions a 3D space until the size of a cell comes to the most approximate size initially set by the user, and then an identification number is assigned to the cell(figure 5).

7 FIG. 5. Octree structure 3.3 Entering data on a Cell The cell information is entered on the central point of each cell with an identification number and expressed as shown in Figure 6. The cell information includes an identification number, soil properties, and earthwork. The earthwork is expressed as 00%C or 00%F, a combination of the ratio of soil in the cell with the work type. To be more specific, 00% describes what percentage of soil is in the cell and C is for cutting while F is for filling. In addition, the equipment location point is on the upper surface in the right direction of the point, and will be utilized in future planning for the earthwork in tandem with the central point of the cell.

8 FIG. 6. Cell data 4. Sending data to the integrated system The information of the cell is once composed and sent to Autonomous Excavation System to prepare an earthwork plan. At this time, the file can be saved and sent in a file format a user can open, such as *.dxf. The data sent is utilized as fundamental data both for path planning of the construction equipment and for fleet-type work planning. SUMMARY AND FUTURE RESEARCH As structures have become larger, the demand on the infrastructures has also increased, and with the gradual increase in demand for exploration of land humans hardly access, such as the polar regions, the deep sea and outer space, the demand for automated earthwork has also been on the rise. To help make automated earthwork a reality, we defined the analysis cell, a basic unit of the analysis of this study, and suggested how to utilize it. The 3D terrain processing platform largely consists of four parts. First, the basic work is carried out by loading the scanned data and the design drawing. Second, the updating soil properties module is presented to update the boring information and soil qualities required for the earthwork planning. Third, the analysis range is limited to set the basic unit of analysis, and then is divided into analysis cells and the creating analysis cell module to enter data on each cell is defined. Finally, the function of sending data to an integrated system is demonstrated. The 3D terrain processing platform is developed as a prototypical software in this study. It is verified with simple scanned data and the planned design. In the verification of the DB obtained through the 3D terrain processing platform, it was found that the required data was included in each cell, such as coordinates, soil qualities, boring information, earthwork volume, cutting or filling, etc. Cell types that reflected ground shape were not set in this study. The cell type should be included in the future to make a task planning for the heavy equipment or construction equipment.

9 ACKNOWLEDGMENTS This research was supported by a grant (14SCIP-B ) from Smart Civil Infrastructure Research Program funded by Ministry of Land, Infrastructure and Transport(MOLIT) of Korea government and Korea Agency for Infrastructure Technology Advancement(KAIA). REFERENCES Chae, M. J., Lee, G. W., Kim, J. R., Park, J. W., Yoo, H. S., Cho, M. Y., (2009) Development of the 3D Imaging System and Automatic Registration Algorithm for the Intelligent Excavation System (IES), Korea Journal of Construction Engineering and Management, 10(1), Chae, M. J., Lee, G. W., Kim, J. Y., Park, J. W. and Cho, M. Y. (2011) A 3D surface modeling system for intelligent excavation system, Automation in Construction, Vol.20, No.7, Du, J.C. and Teng, H.C. (2007) 3D laser scanning and GPS technology for landslide earthwork volume estimation. Automation in Construction, 16(5), Hashash, Y. M. and Finno, R. J. (2008) Development of new integrated tools for predicting, monitoring, and controlling ground movements due to excavations, Practice Periodical on Structural Design and Construction, Vol.13, No.1, Hashash, Y., Oliveira Filho, J., Su, Y., and Liu, L. (2005) 3D laser scanning for tracking supported excavation construction, Geo-Frontiers 2005, Kim, S. K., Seo, J. W. and Russell, J. S. (2011) Intelligent navigation strategies for an automated earthwork system. Automation in Construction, 21, Lever, P. Wang, F. (1995) Intelligent Excavator Control System for Lunar Mining System. Journal of Aerospace Engineering, 8(1), Marechal, L. (2009), Advances in octree-based allhexahedral mesh generation: handling sharp features, 18th International Meshing Roundtable, Salt Lake City, UT, USA, Park, J. J., Shin, J. H., Hwang, J. H., Lee, K. H., Seo, H. J. and Lee, I. M. (2012) Assessment of over / under-break of tunnel utilizing BIM and 3D laser Scanner, Journal of Korean Tunneling and Underground Space Association, 14(4), Park, S. H., Choi, D. H., Han, D. Y., (2009) Deformation Measurements of Dam using Terrestrial Laser Scanner, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol.27, No.1, Pejić, M. (2013) Design and optimisation of laser scanning for tunnels geometry inspection, Tunnelling and Underground Space Technology, Vol.37, Prokop, A. and Panholzer, H. (2009) Assessing the capability of terrestrial laser scanning for monitoring slow moving landslides", Natural Hazards and Earth System Science, Vol.9, No.6, Saxena, M., Finnigan, P. M., Graichen, C. M., Hathaway, A. F. and Parthasarathy, V. N. (1995), Octree-based automatic mesh generation for non-manifold domains, Engineering with Computers, Vol. 11, 1-14.

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