Hostname: page-component-7dd5485656-wlg5v Total loading time: 0 Render date: 2025-10-26T15:43:16.817Z Has data issue: false hasContentIssue false
  • English
  • Français

Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey

Published online by Cambridge University Press:  25 March 2022

Huynh A.D. Nguyen  and
Quang P. Ha Open the ORCID record for Quang P. Ha [Opens in a new window]
Huynh A.D. Nguyen
Affiliation:
Faculty of Engineering and Information Technology, University of Technology Sydney, Australia, Sydney
Quang P. Ha*
Affiliation:
Faculty of Engineering and Information Technology, University of Technology Sydney, Australia, Sydney
*
*Corresponding author. E-mail: quang.ha@uts.edu.au
Get access Rights & Permissions [Opens in a new window]

Abstract

There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.

Keywords

robotic autonomous systemsearthmoving equipmentvolatile conditionsteaming capacityoriginal equipment manufacturers

Information

Type
Research Article
Information
Robotica , Volume 41 , Issue 2 , February 2023 , pp. 486 - 510
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Naghshbandi, S. N., Varga, L. and Hu, Y., “Technologies for safe and resilient earthmoving operations: A systematic literature review,” Automat. Constr. 125(1), 103632 (2021).CrossRefGoogle Scholar
Ha, Q., Yen, L. and Balaguer, C., “Robotic autonomous systems for earthmoving in military applications,” Automat. Constr. 107(13), 102934 (2019).CrossRefGoogle Scholar
Melenbrink, N., Werfel, J. and Menges, A., “On-site autonomous construction robots: Towards unsupervised building,” Automat. Constr. 119(3), 103312 (2020).CrossRefGoogle Scholar
Wong, C., Yang, E., Yan, X.-T. and Gu, D., “Autonomous robots for harsh environments: a holistic overview of current solutions and ongoing challenges,” Syst. Sci. Control Eng. 6(1), 213–219 (2018).CrossRefGoogle Scholar
Ballesteros-Pérez, P., Rojas-Céspedes, Y. A., Hughes, W., Kabiri, S., Pellicer, E., Mora-Melià, D. and del Campo-Hitschfeld, M. L., “Weather-wise: A weather-aware planning tool for improving construction productivity and dealing with claims,” Automat. Constr. 84(2006), 81–95 (2017).CrossRefGoogle Scholar
Azar, E. R. and Kamat, V. R., “Earthmoving equipment automation: A review of technical advances and future outlook,” J. Inform. Technol. Construct. (ITcon) 22(13), 247–265 (2017).Google Scholar
Casey, J. P., Two autonomous trucks collide in heavy rain at BHP’s Jimblebar hub, 2019-07, accessed 20/06/2020, [Online]. Available: https://www.mining-technology.com/mining-safety/two-autonomous-trucks-collide-in-heavy-rain-at-bhps-jimblebar-hub,.Google Scholar
Marr, B., The 4 ds of robotization: dull, dirty, dangerous and dear, accessed 20/06/2020, Oct. 16, 2017. [Online]. Available: https://www.forbes.com/sites/bernardmarr/2017/10/16/the-4-ds-of-robotization-dull-dirty-dangerous-and-dear/?sh=6bfa96463e0d Google Scholar
Czarnowski, J., Dabrowski, A., Maciaś, M., Główka, J. and Wrona, J., “Technology gaps in human-machine interfaces for autonomous construction robots,” Automat. Constr. 94, 179–190 (2018).CrossRefGoogle Scholar
report, K., Starting the new Mid-Term management plan (FY2019-2021) online, accessed 22/02/2021, 2019. [Online]. Available: https://home.komatsu/en/press/2019/management/1202321_1833.html Google Scholar
Delgado, J. M. D., Oyedele, L., Ajayi, A., Akanbi, L., Akinade, O., Bilal, M. and Owolabi, H., “Robotics and automated systems in construction: Understanding industry-specific challenges for adoption,” J. Build. Eng. 26, 100868 (2019).CrossRefGoogle Scholar
Durst, P. J. and Gray, W. (2014-04). Levels of autonomy and autonomous system performance assessment for intelligent unmanned systems, online, accessed 11/06/2021 [Online]. Available: https://erdc-library.erdc.dren.miljspui/bitstream/11681/3284/1/ERDC-GSL-SR-14-1.pdf Google Scholar
IFR, IFR forecast: 1.7 million new robots to transform the world’s factories by 2020, online, accessed 11/11/2020, 2017. [Online]. Available: https://ifr.org/downloads/press/English_Press_Release_IFR_World_Robotics_Report_2017-09-27.pdf Google Scholar
Kim, P., Chen, J., Kim, J. and Cho, Y. K., “Slam-Driven Intelligent Autonomous Mobile Robot Navigation for Construction Applications,” In: Workshop of the European Group for Intelligent Computing in Engineering, (Springer, 2018) pp. 254–269.Google Scholar
Lopesa, J. and Trabancoa, J., “Automated control systems for civil construction machinery using rtk-gnss: an implementation review,” Revista de Engenharia 56, 28–33 (2019).Google Scholar
Xu, L., Feng, C., Kamat, V. R. and Menassa, C. C., “Enhancing Visual Slam with Occupancy Grid Mapping for Real-Time Locating Applications in Indoor GPS-denied Environments,” In: Computing in Civil Engineering 2019: Data, Sensing, and Analytics (American Society of Civil Engineers, Reston, VA, 2019) pp. 344–351.CrossRefGoogle Scholar
Ali, I., Durmush, A., Suominen, O., Yli-Hietanen, J., Peltonen, S., Collin, J. and Gotchev, A., “Finnforest dataset: A forest landscape for visual slam,” Robot. Auton. Syst. 132(5), 103610 (2020).CrossRefGoogle Scholar
Lu, Y. and Song, D., “Visual navigation using heterogeneous landmarks and unsupervised geometric constraints,” IEEE Trans. Robot. 31(3), 736–749 (2015).CrossRefGoogle Scholar
Kim, P., Chen, J. and Cho, Y. K., “Slam-driven robotic mapping and registration of 3D point clouds,” Automat. Constr. 89, 38–48 (2018).CrossRefGoogle Scholar
Huang, F., Yang, H., Tan, X., Peng, S., Tao, J. and Peng, S., “Fast reconstruction of 3D point cloud model using visual slam on embedded uav development platform,” Remote Sens.-BASEL 12(20), 3308 (2020).CrossRefGoogle Scholar
Zhao, Z., Mao, Y., Ding, Y., Ren, P. and Zheng, N., “Visual-based Semantic Slam with Landmarks for Large-Scale Outdoor Environment,” In: 2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI) (IEEE 2019) pp. 149–154.CrossRefGoogle Scholar
Wu, C., Wang, X., Chen, M. and Kim, M. J., “Differential received signal strength based positioning for construction equipment tracking,” Adv. Eng. Inform. 42(1), 100960 (2019).CrossRefGoogle Scholar
Louis, J. and Dunston, P. S., “Integrating iot into operational workflows for real-time and automated decision-making in repetitive construction operations,” Automat. Constr. 94(2), 317–327 (2018).CrossRefGoogle Scholar
Nilwong, S., Hossain, D., Kaneko, S.-i and Capi, G., “Deep learning-based landmark detection for mobile robot outdoor localization,” Machines 7(2), 25 (2019).CrossRefGoogle Scholar
Sung, C. and Kim, P. Y., “3D terrain reconstruction of construction sites using a stereo camera,” Automat. Constr. 64(6), 65–77 (2016).CrossRefGoogle Scholar
Nevalainen, N. and Pellinen, T., “The use of a thermal camera for quality assurance of asphalt pavement construction,” Int J Pavement. Eng. 17(7), 626–636 (2016).CrossRefGoogle Scholar
Ha, Q., Watanabe, K., Karasawa, T., Ushiku, Y. and Harada, T., “Mfnet: Towards Real-Time Semantic Segmentation for Autonomous Vehicles with Multi-spectral Scenes,” In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2017) pp. 5108–5115.CrossRefGoogle Scholar
Morales, A., Guerra, R., Horstrand, P., Diaz, M., Jimenez, A., Melian, J., Lopez, S. and Lopez, J., “A multispectral camera development: From the prototype assembly until its use in a UAV system,” Sensors 20(21), 6129 (2020).CrossRefGoogle Scholar
Dadhich, S., Bodin, U. and Andersson, U., “Key challenges in automation of earth-moving machines,” Automat. Constr. 68(1), 212–222 (2016).CrossRefGoogle Scholar
Ren, Z., Wang, L. and Bi, L., “Robust GICP-Based 3D LiDAR SLAM for underground mining environment,” Sensors 19(13), 2915 (2019).CrossRefGoogle ScholarPubMed
Dang, T., Mascarich, F., Khattak, S., Nguyen, H., Nguyen, H., Hirsh, S., Reinhart, R., Papachristos, C. and Alexis, K., “Autonomous Search for Underground Mine Rescue Using Aerial Robots,” In: 2020 IEEE Aerospace Conference (IEEE, 2020) pp. 1–8.CrossRefGoogle Scholar
Jacobson, A., Zeng, F., Smith, D., Boswell, N., Peynot, T. and Milford, M., “What localizes beneath: A metric multisensor localization and mapping system for autonomous underground mining vehicles,” J. Field Robot. 38(1), 5–27 (2021).CrossRefGoogle Scholar
Sun, D., Baek, I., Hwang, S., Lee, S., Lee, S.-K., Jang, S., Ji, C., Han, J. and Han, C., “Sensor-based straight-line control of the end-point of a typical retrofitted hydraulic excavator,” Automat. Constr. 120(2), 103385 (2020).CrossRefGoogle Scholar
Jiang, Y. and He, X., “Overview of applications of the sensor technologies for construction machinery,” IEEE Access 8, 324–335 (2020).Google Scholar
Sun, D.-I., Kim, S. H., Lee, Y. S., Lee, S. K. and Han, C. S., “Pose and Position Estimation of Dozer Blade in 3-dimensional by Integration of IMU with Two RTK GPSS,” In: ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC Publications, vol., 34, 2017).CrossRefGoogle Scholar
Soltani, M. M., Zhu, Z. and Hammad, A., “Framework for location data fusion and pose estimation of excavators using stereo vision,” J. Comput. Civil Eng. 32(6), 04018045 (2018).CrossRefGoogle Scholar
Komatsu, Intelligent machine control - INNOVATIVE; INTELLIGENT; INTEGRATED, accessed 04/05/2021, [Online]. Available: https://www.komatsu.com.au/getattachment/433a2c80-9127-4f26-966f-e5d459bcf0e8/iMC-Technology Google Scholar
Farajiparvar, P., Ying, H. and Pandya, A., “A brief survey of telerobotic time delay mitigation,” Front. Robot. AI 7, 198 (2020).CrossRefGoogle ScholarPubMed
Jud, D., Hottiger, G., Leemann, P. and Hutter, M., “Planning and control for autonomous excavation,” IEEE Robot. Automat. Lett. 2(4), 2151–2158 (2017).CrossRefGoogle Scholar
Fernando, H., Marshall, J. A. and Larsson, J., “Iterative learning-based admittance control for autonomous excavation,” J. Intell. Robot. Syst. 96(3), 493–500 (2019).CrossRefGoogle Scholar
Zhang, L., Zhao, J., Long, P., Wang, L., Qian, L., Lu, F., Song, X. and M., D., “An autonomous excavator system for material loading tasks,” Sci. Robot. 6(55), eabc3164 (2021).CrossRefGoogle ScholarPubMed
Staff, A., CAT, volvo invest in remote control capabilities, accessed 20/08/2020, 2017. [Online]. Available: https://theasphaltpro.com/articles/cat-volvo-invest-in-remote-control-capabilities/ Google Scholar
Bennink, C., 5G Enables Advances in Machine Connectivity. accessed 22/03/2021, Feb. 10, 2020. [Online]. Available: https://www.forconstructionpros.com/construction-technology/article/21111112/5g-enables-advances-in-machine-connectivity Google Scholar
Yang, Y. and Hua, K., “Emerging technologies for 5g-enabled vehicular networks,” IEEE Access 7, 181 117–181 141 (2019).CrossRefGoogle Scholar
Ordonez, C., Gupta, N., Reese, B., Seegmiller, N., Kelly, A. and Collins, E. G. Jr., “Learning of skid-steered kinematic and dynamic models for motion planning,” Robot. Auton. Syst. 95(2), 207–221 (2017).CrossRefGoogle Scholar
Alexander, A. and Vacca, A., “Real-Time Parameter Setpoint Optimization for Electro-Hydraulic Traction Control Systems,” In: Proceedings of 15: th Scandinavian International Conference on Fluid Power, June 7-9 (Linköping University Electronic Press, 2017) pp. 104–114.Google Scholar
Özdemir, M. N., Kılıç, V. and Ünlüsoy, Y. S., “A new contact & slip model for tracked vehicle transient dynamics on hard ground,” J. Terramechanics 73(1), 3–23 (2017).CrossRefGoogle Scholar
Lu, H., Xiong, G. and Guo, K., “Motion predicting of autonomous tracked vehicles with online slip model identification,” Math. Probl. Eng. ID 6375652, 13 pages (2016).Google Scholar
Yamauchi, G., Nagatani, K., Hashimoto, T. and Fujino, K., “Slip-compensated odometry for tracked vehicle on loose and weak slope,” Robomech. J. 4(1), 1–11 (2017).CrossRefGoogle Scholar
Marin, L., “Dynamic three-dimensional simulation model of soil interaction estrecha narrow tillage tool using the finite element method,” MOJ Curr. Res. Rev. 1 (2020).Google Scholar
Haeri, A., Tremblay, D., Skonieczny, K., Holz, D. and Teichmann, M., “Efficient Numerical Methods for Accurate Modeling of Soil Cutting Operations,” In: Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC) (International Association for Automation and Robotics in Construction (IAARC), Kitakyushu, Japan, October 2020) pp. 608–615.CrossRefGoogle Scholar
Bui, D. T., Nhu, V.-H. and Hoang, N.-D., “Prediction of soil compression coefficient for urban housing project using novel integration machine learning approach of swarm intelligence and multi-layer perceptron neural network,” Adv. Eng. Inform. 38(Part 1), 593–604 (2018).Google Scholar
Narayanan, A. and Bhojne, S., Construction equipment’s bucket design based on soil-tool interaction-analytical and dem approach. SAE Technical Paper, Tech. Rep. (2017).CrossRefGoogle Scholar
Boudaa, S., Khalfallah, S. and Hamioud, S., “Dynamic analysis of soil structure interaction by the spectral element method,” Innov. Infrastruct. Sol. 4(1), 1–8 (2019).Google Scholar
Reginald, N., Seo, J. and Rasul, A., “Development of an Integrated Tracking Control Algorithm for Digging Operations of An Excavator,” In: 2020 20th International Conference on Control, Automation and Systems (ICCAS) (IEEE, 2020) pp. 195–200.CrossRefGoogle Scholar
Narayanan, A. and Bhojne, S., “Construction equipment’s bucket design based on soil-tool interaction-analytical and dem approach,” SAE Technical Paper, Tech. Rep. (2017).CrossRefGoogle Scholar
Kenarsari, A. E., Vitton, S. J. and Beard, J. E., “Tactile pressure sensors to measure ground pressure from tractor tire loads,” Geotech. Test. J. 41(6), 1166–1174 (2018).CrossRefGoogle Scholar
Mattetti, M., Varani, M., Molari, G. and Morelli, F., “Influence of the speed on soil-pressure over a plough,” Biosyst. Eng. 156(1), 136–147 (2017).CrossRefGoogle Scholar
Rasul, A., Seo, J. and Khajepour, A., “Development of integrative methodologies for effective excavation progress monitoring,” Sensors 21(2), 364 (2021).CrossRefGoogle ScholarPubMed
Yuzugullu, O., Lorenz, F., Fröhlich, P. and Liebisch, F., “Understanding fields by remote sensing: soil zoning and property mapping,” Remote Sens.-BASEL 12(7), 1116 (2020).CrossRefGoogle Scholar
Bennett, N., Walawalkar, A., Heck, M. and Schindler, C., “Integration of digging forces in a multi-body-system model of an excavator,” Proc. Inst. Mech. Eng. K J. Multi-body Dynam. 230(2), 159–177 (2016).Google Scholar
Zhao, Y., Wang, J., Zhang, Y. and Luo, C., “A novel method of soil parameter identification and force prediction for automatic excavation,” IEEE Access 8, 11 197–11 207 (2020).CrossRefGoogle Scholar
America, K., PRESS RELEASE - komatsu america Corp. introduces the new GD655-7 motor grader, accessed 20/08/2020, Mar. 11, 2019. [Online]. Available: https://www.komatsuamerica.com/our-company/press-releases/2019-03-11-gd655-7 Google Scholar
Europe, C. E., Productivity services - power your productivities, accessed 20/01/2021, 2020. [Online]. Available: https://www.volvoce.com/europe/en/services/volvo-services/productivity-servicesGoogle Scholar
Mining, A., Sandvik, IBM partner to bring advanced analytics for mining sector, accessed 20/08/2020, Mar. 13, 2017. [Online]. Available: https://www.australianmining.com.au/news/sandvik-ibm-partner-bring-advanced-analytics-mining-sector/ Google Scholar
Volvo. PAVE ASSIST - UNLOCK FIRST-CLASS PAVING RESULTS (2020). accessed 20/02/2021[Online]. Available: https://www.volvoce.com/europe/en/services/volvo-services/productivity-services/pave-assist/ Google Scholar
Nguyen, H., Nguyen, L. and Ha, Q., “Iot-Enabled Dependable Co-located Low-Cost Sensing for Construction Site Monitoring,” In: 37th International Symposium on Automation and Robotics in Construction. International Association for Automation and Robotics in Construction (IAARC) (2020).CrossRefGoogle Scholar
Schmidt, D. and Berns, K., “Construction Site Navigation for the Autonomous Excavator Thor,” In: 2015 6th International Conference on Automation, Robotics and Applications (ICARA) (IEEE, 2015) pp. 90–97.CrossRefGoogle Scholar
Stolfi, D. H., Brust, G. D. and Bouvry, P., “UAV-UGV-UMV multi-swarms for cooperative surveillance,” Front. Robot. AI 8, Article 616950 (2021).CrossRefGoogle ScholarPubMed
Bu, X., Yu, Q., Hou, Z. and Qian, W., “Model free adaptive iterative learning consensus tracking control for a class of nonlinear multiagent systems,” IEEE Trans. Syst. Man Cybernet. Syst. 49(4), 677–686 (2017).CrossRefGoogle Scholar
Alonso-Mora, J., Beardsley, P. and Siegwart, R., “Cooperative collision avoidance for nonholonomic robots,” IEEE Trans. Robot 34(2), 404–420 (2018).CrossRefGoogle Scholar
Huang, C. and Liu, R., “Inner attention supported adaptive cooperation for heterogeneous multi robots teaming based on multi-agent reinforcement learning,” arXiv preprint arXiv:2002.06024, 2020.Google Scholar
Kshirsagar, J., Shue, S. and Conrad, J. M., “A Survey of Implementation of Multi-robot Simultaneous Localization and Mapping,” In: SoutheastCon 2018 (IEEE, 2018) pp. 1–7.Google Scholar
Masehian, E., Jannati, M. and Hekmatfar, T., “Cooperative mapping of unknown environments by multiple heterogeneous mobile robots with limited sensing,” Robot. Auton. Syst. 87(3–4), 188–218 (2017).CrossRefGoogle Scholar
Moon, D., Chung, S., Kwon, S., Seo, J. and Shin, J., “Comparison and utilization of point cloud generated from photogrammetry and laser scanning: 3d world model for smart heavy equipment planning,” Automat. Constr. 98(2), 322–331 (2019).CrossRefGoogle Scholar
Komatsu, SMARTCONSTRUCTION, accessed 10/11/2020, Dec. 17 2018. [Online]. Available: https://www.komatsu.com.au/getattachment/7aa7979d-8a9b-4119-af96-8acc9cf44754/SMARTCONSTRUCTION Google Scholar
Lee, G. and Chwa, D., “Decentralized behavior-based formation control of multiple robots considering obstacle avoidance,” Intel. Serv. Robot. 11(1), 127–138 (2018).CrossRefGoogle Scholar
Lavery, K., Convoy of Connectivity: U.S. Army Tests Autonomous Trucks in Michigan. online, accessed 20/03/2021, Nov. 8, 2017. [Online]. Available: https://www.wkar.org/post/convoy-connectivity-us-army-tests-autonomous-trucks-michigan#stream/0 Google Scholar
Ebel, H. and Eberhard, P., “A comparative look at two formation control approaches based on optimization and algebraic graph theory,” Robot. Auton. Syst. 136(7), 103686 (2021).CrossRefGoogle Scholar
Ji, A., Xue, X., Ha, Q., Luo, X. and Zhang, M., “Game theory–based bilevel model for multiplayer pavement maintenance management,” Automat. Constr. 121(4), 103763 (2021).CrossRefGoogle Scholar
Phung, M. D. and Ha, Q. P., “Motion-encoded particle swarm optimization for moving target search using uavs,” Appl. Soft Comput. 97(3), 106705 (2020).CrossRefGoogle Scholar
Mining, C. G. (2015-11). [VIDEO] Cat $^{\circledR }$ Mining Product demonstration. online, accessed 22/03/2021. [Online]. Available: https://www.youtube.com/watch?app=desktop&v=iS-gUL9xoAU Google Scholar
Tang, S., Shelden, D. R., Eastman, C. M., Pishdad-Bozorgi, P. and Gao, X., “A review of building information modeling (bim) and the internet of things (IoT) devices integration: Present status and future trends,” Automat. Constr. 101(66), 127–139 (2019).CrossRefGoogle Scholar
Bucchiarone, A., Sanctis, M. D., Hevesi, P., Hirsch, M., Abancens, F. J. R., Vivanco, P. F., Amiraslanov, O. and Lukowicz, P., “Smart construction: remote and adaptable management of construction sites through iot,” IEEE Internet Things Mag. 2(3), 38–45 (2019).CrossRefGoogle Scholar
Khokale, S., How IoT is making heavy equipment safer and more efficient, 2019-03-19, accessed 22/04/2021, [Online]. Available: https://www.einfochips.com/blog/how-iot-is-making-heavy-equipment-safer-and-more-efficient/ Google Scholar
Reja, V. K. and Varghese, K., “Impact of 5G Technology on IoT Applications in Construction Project Management?,” In: Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC) (International Association for Automation and Robotics in Construction (IAARC), Banff, Canada, May 2019) pp. 209–217.Google Scholar
Kim, J., Lee, S. S., Seo, J. and Kamat, V. R., “Modular data communication methods for a robotic excavator,” Automat. Constr. 90(7), 166–177 (2018).CrossRefGoogle Scholar
Corporation, N. E. C., KDDI, obayashi, and NEC use 5G to successfully remotely control construction machinery in a cooperative operation, accessed 05/01/2021, Jan. 28, 2019. [Online]. Available: https://www.nec.com/en/press/201901/global_20190128_01.html?fbclid=IwAR1i28Tm7_AEPiCOSjSMOFRpwsohx4xp1-g3efh55Lfc4gsy3_EhZ58GX2AGoogle Scholar
Kim, J., Chi, S. and Seo, J., “Interaction analysis for vision-based activity identification of earthmoving excavators and dump trucks,” Automat. Constr. 87(6), 297–308 (2018).Google Scholar
Arabi, S., Haghighat, A. and Sharma, A., “A deep-learning-based computer vision solution for construction vehicle detection,” Comput.-AIDED Civ. Inf. 35(7), 753–767 (2020).CrossRefGoogle Scholar
Kim, J. and Chi, S., “Action recognition of earthmoving excavators based on sequential pattern analysis of visual features and operation cycles,” Automat. Constr. 104, 255–264 (2019).CrossRefGoogle Scholar
Xiao, B., Lin, Q. and Chen, Y., “A vision-based method for automatic tracking of construction machines at nighttime based on deep learning illumination enhancement,” Automat. Constr. 127, 103721 (2021).CrossRefGoogle Scholar
Zhu, Q., Dinh, T. H., Phung, M. D. and Ha, Q. P., “Hierarchical convolutional neural network with feature preservation and autotuned thresholding for crack detection,” IEEE Access 9, 60 201–60 214 (2021).CrossRefGoogle Scholar
Ma, J. W., Czerniawski, T. and Leite, F., “Semantic segmentation of point clouds of building interiors with deep learning: augmenting training datasets with synthetic bim-based point clouds,” Automat. Constr. 113(1), 103144 (2020).CrossRefGoogle Scholar
Khallaf, R. and Khallaf, M., “Classification and analysis of deep learning applications in construction: A systematic literature review,” Automat. Constr. 129, 103760 (2021).CrossRefGoogle Scholar
Sartoretti, G., Wu, Y., Paivine, W., Kumar, T. S., Koenig, S. and Choset, H., “Distributed Reinforcement Learning for Multi-robot Decentralized Collective Construction,” In: Distributed Autonomous Robotic Systems (Springer, 2019) pp. 35–49.CrossRefGoogle Scholar
Frank, M., Ruvald, R., Johansson, C., Larsson, T. and Larsson, A., “Towards autonomous construction equipment-supporting on-site collaboration between automatons and humans,” Int. J. Prod. Develop. 23(4), 292–308 (2019).CrossRefGoogle Scholar
Fretty, P., Smart construction - AI Style. online, accessed 05/05/2021, Oct. 23, 2020. [Online]. Available: https://www.industryweek.com/technology-and-iiot/article/21145580/smart-construction-ai-style Google Scholar
NVIDIA, Japan’s komatsu selects NVIDIA as partner for deploying AI to create safer, more efficient construction sites, accessed 22/03/2021, Dec. 12, 2017. [Online]. Available: https://nvidianews.nvidia.com/news/japans-komatsu-selects-nvidia-as-partner-for-deploying-ai-to-create-safer-more-efficient Google Scholar
Cosbey, A., Mann, H., Maennling, N., Toledano, P., Geipel, J. and Brauch, M. D., Mining a mirage: reassessing the shared-value paradigm in light of the technological advances in the mining sector (2016). [Online]. Available: https://www.iisd.org/sites/default/files/publications/mining-a-mirage.pdf.Google Scholar
NSW Resources Regulator, Collision between semi-autonomous dozer and an excavator, accessed 05/05/2021, Oct. 2, 2019. [Online]. Available: https://www.resourcesregulator.nsw.gov.au/data/assets/pdf_file/0005/1183586/Causal-investigation-Collision-between-semi-autonomous-dozer-and-excavator.pdf Google Scholar
Caterpillar, Comparison Report - Caterpillar Jobsite Information Study, accessed 04/02/2021, 2017. [Online]. Available: file: C:/Hubert/RAS_survey/document-cat-connect-white-paper-productivity-52019.pdfGoogle Scholar
Stone, T., “Hitachi’s first semi-autonomous excavator,” accessed 04/04/2021, Feb. 1, 2019. [Online]. Available: https://www.ivtinternational.com/videos/hitachis-first-semi-autonomous-excavator.html Google Scholar
Volvo, Driving prosperity” volvo group annual &. Sustainability Report 2018,”, accessed 04/04/2021, Mar. 12, 2019. [Online]. Available: https://www.volvogroup.com/en/news-and-media/events/2019/mar/annual-and-sustainability-report-2018.html Google Scholar
Sharry, Z. (2018-10-25). Top ten heavy equipment manufacturers. accessed 04/05/2021. [Online]. Available: https://blog.iseekplant.com.au/blog/top-ten-heavy-equipment-manufacturers Google Scholar
Cullinane, D., Mining industry about to be transformed by automation and robotics, 2019-09-17, accessed 02/03/2021, [Online]. Available: https://smallcaps.com.au/mining-industry-transformation-automation-robotics/.Google Scholar
Lambert, F., Caterpillar unveils an all-electric 26-ton excavator with a giant 300 kWh battery pack, accessed 20/03/2021, Jan. 29, 2019. [Online]. Available: https://electrek.co/2019/01/29/caterpillar-electric-excavator-giant-battery-pack/ Google Scholar
Volvo, The World’s First ‘Free Emission’ Quarry. accessed 04/01/2021, 2018. [Online]. Available: https://www.volvoce.com/global/en/this-is-volvo-ce/what-we-believe-in/innovation/electric-site/ Google Scholar