Path-Optimizing Agent-Based Earthwork System: a Microscopically Precise Earthwork System that Is Adaptable to Any Form of Landscape

  • Zixiao Ji
  • Yuqiong LinEmail author
Conference paper


Along with the development of the landscape design and robotic technology, the traditional methods of the earthwork for the landscape can no longer adapt to highly artistic and customized landscape due to the overwhelming in term of time, effort and cost, as well as the low precision to conduct micro-level earthwork. Thus, the automation of the earthwork operations has been widely studied. This paper introduces an autonomous earthwork system which takes one or several robot agents to detect the difference between the initial terrain and the target terrain, and gradually shape the initial terrain toward the target terrain so that in the end the target terrain is formed from the initial terrain automatically. Different levels of intelligence are added to the system including path-optimizing with mathematic calculation, so that the robot agents are able to consider the factors of efficiency and time when they perform their function, and makes the system more precise and adaptable.


Autonomous earthwork system Agent-based Path-optimizing Robot agent 


  1. 1.
    Zafar, M.N., Mohanta, J.C.: Methodology for path planning and optimization of mobile robots: a review. Procedia Comput. Sci. 133, 141–152 (2018)CrossRefGoogle Scholar
  2. 2.
    Raja, P., Pugazhenthi, S.: Optimal path planning of mobile robots: a review. Int. J. Phys. Sci. 7(9), 1314–1320 (2012)CrossRefGoogle Scholar
  3. 3.
    Ratajczak-Ropel, E., Skakovski, A.: Population-Based Approaches to the Resource-Constrained and Discrete-Continuous Scheduling. Springer (2018)Google Scholar
  4. 4.
    Kim, S.-K., Seo, J., Russell, J.S.: Intelligent navigation strategies for an automated earthwork system. Autom. Constr. 21, 132–147 (2012)CrossRefGoogle Scholar
  5. 5.
    Rohmer, E., Singh, S.P.N., Freese, M.: V-REP: a versatile and scalable robot simulation framework. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. (2013)Google Scholar
  6. 6.
    Robotics, C. V-rep, virtual robot experimentation platform (2018).

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.GSAPP, Columbia UniversityNew YorkUSA
  2. 2.CAUP, Tongji UniversityShanghaiChina

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