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

Author:

Nguyen Huynh A.D.,Ha Quang P.ORCID

Abstract

AbstractThere 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.

Publisher

Cambridge University Press (CUP)

Subject

Computer Science Applications,General Mathematics,Software,Control and Systems Engineering,Control and Optimization,Mechanical Engineering,Modeling and Simulation

Reference108 articles.

1. A deep-learning-based computer vision solution for construction vehicle detection;Arabi;Comput.-AIDED Civ. Inf.,2020

2. [85] 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/

3. Visual navigation using heterogeneous landmarks and unsupervised geometric constraints;Lu;IEEE Trans. Robot.,2015

4. [43] 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

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Real-time trajectory planning for asphalt compaction operator support;Automation in Construction;2023-11

2. Digital twin-based excavation trajectory generation of Uncrewed excavators for autonomous mining;Automation in Construction;2023-07

3. Aggregate-Forming Planner for Autonomous Earth-Moving;IEEE Access;2023

4. Design and Development of an Unmanned Excavator System for Autonomous Mining;Conference Proceedings of 2022 2nd International Joint Conference on Energy, Electrical and Power Engineering;2023

5. Single Frame Lidar-Camera Calibration Using Registration of 3D Planes;2022 Sixth IEEE International Conference on Robotic Computing (IRC);2022-12

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3