Modeling of Three-Dimensional Terrain Data for Intelligent Control of Unmanned Construction Machine in Deterministic Surfacing Process

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Abstract:

Deterministic surfacing technique is an effective method for intelligent control of operation planning of the unmanned construction machine, and its foundation is the accurate modeling of the three-dimensional terrain data. According to the digital elevation model based on regular grid, the modeling of 3D terrain surface data is obtained. The evaluation of residual error is further investigated, which is quite important for the iterated operation in deterministic surfacing. Through revealing flow chart of the deterministic surfacing method, the importance of the modeling of terrain data and that of the evaluation of residual error are emphasized. The study on modeling of terrain data will promote application of the deterministic surfacing in intelligent control of unmanned construction machine.

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Advanced Materials Research (Volumes 1073-1076)

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1917-1921

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December 2014

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] Y Hiramatsu, T Aono, M Nishio: Advanced Robotics Vol. 16 (2002), pp.505-508.

Google Scholar

[2] Boyd C. Paulson, Jr.: J. Constr. Eng. Manage. Vol. 111 (1995), p.190–207.

Google Scholar

[3] BJ Alshaer, TT Darabseh, MA Alhanouti: Applied Mathematical Modelling Vol. 37 (2013), p.5315–5325.

DOI: 10.1016/j.apm.2012.10.042

Google Scholar

[4] H Yamada, G Ming-de, Z Dingxuan: Journal of Robotics and Mechatronics Vol. 19 (2007), pp.60-67, (2007).

Google Scholar

[5] XM Shen, YF Dai, H Deng, C Guan, K Yamamura: Opt. Express Vol. 21 (2013), pp.26123-26135.

Google Scholar

[6] XM Shen, M Nagano, WQ Peng, YF Dai, and K Yamamura: Key Engineering Materials Vol. 523-524 (2012), pp.276-280.

Google Scholar

[7] XM Shen, YF Dai, WQ Peng, M Nagano, and K Yamamura: Key Engineering Materials Vol. 516 (2012), pp.504-509.

Google Scholar

[8] M Christen, J Kowalski, P Bartelt: Cold Regions Science and Technology Vol. 63 (2010), pp.1-14.

Google Scholar

[9] ZR Detweiler, JB Ferris: Journal of Terramechanics Vol. 47 (2010), pp.209-217.

Google Scholar

[10] J Jin, L Tang: Journal of Field Robotics Vol. 28 (2011), p.424–440.

Google Scholar

[11] W Zhang, DR Montgomery: Water resources research Vol. 30 (1994), pp.1019-1028.

Google Scholar

[12] DM Wolock, CV Price: Water Resources Research Vol. 30 (1994), pp.3041-3052.

Google Scholar

[13] C Hladik, M Alber: Remote Sensing of Environment Vol. 121 (2012), pp.224-235.

Google Scholar

[14] F Pan, J Nichols: Hydrological Processes Vol. 27 (2012), pp.3596-3606.

Google Scholar

[15] JP Wilson: Geomorphology Vol. 137 (2012), pp.107-121.

Google Scholar

[16] D Lamsal, T Sawagaki, T Watanabe: Journal of Mountain Science Vol. 8 (2011), pp.390-402.

Google Scholar

[17] C Hirt: Journal of Geodesy Vol. 84 (2012), pp.179-190.

Google Scholar

[18] S Roux, F Brun, D Wallach: European Journal of Agronomy Vol. 52 (2014), pp.191-197.

Google Scholar

[19] OP Ferreira, BF Svaiter: Journal of Complexity Vol. 28 (2012), pp.346-363.

Google Scholar