International Journal of Aeronautical and Space Sciences

  • 제10권2호
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  • Pages.1-11
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  • 2009
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  • 2093-274X(pISSN)
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  • 2093-2480(eISSN)
한국항공우주학회 (The Korean Society for Aeronautical and Space Sciences)
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Study on Path Planning Algorithms for Unmanned Agricultural Helicopters in Complex Environment

  • 발행 : 2009.11.30
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초록

In this paper, two algorithms to solve the path planning problem with constraints from obstacles are presented. One proposed Algorithm is "Grid point-based path planning". The first step of this algorithm is to set points which can be the waypoints around the field. These points can be located inside or outside of the field or the obstacles. Therefore, we should determine whether those points are located in the field or not. Using the equations of boundary lines for a region that we are interested in is an effective approach to handle. The other algorithm is based on the boundary lines of the agricultural field, and the concept of this algorithm is well known as "boustrophedon method". These proposed algorithms are simple but powerful for complex cases since it can generate a plausible path for the complex shape which cannot be represented by using geometrical approaches efficiently and for the case that some obstacles or forbidden regions are located on the field by using a skill of discriminants about set points. As will be presented, this proposed algorithm could exhibit a reasonable accuracy to perform an agricultural mission.

키워드

참고문헌

  1. Cao, Z. L., Huang, Y., and Hall, E. L., 1988, “Region filling operations with random obstacle avoidance for mobile robotics”, Journal of Robotic Systems, 5(2), 87-102. https://doi.org/10.1002/rob.4620050202
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  3. Choset, H., and Pignon, P., 1997, “Coverage path planning : The boustrophedon cellular decomposition”, International Conference on Field and Service Robotics, Canberra, Australia.
  4. Sorensen, C.G., Bak and T., Jorgensen, R.N., 2004, “Mission Planner for Agricultural Robotics”, Leuven, Belgium.
  5. Stoll, A., 2003, “Automatic operation planning for GPS-guided machinery”, Proceedings of 4th European Conference on Precision Agriculture, Berlin, Germany.
  6. Oksanen, T., and Visala, A., 2009, “Coverage path planning algorithms for agricultural field machines”, Journal of Field Robotics 26(8), 651-668. https://doi.org/10.1002/rob.20300
  7. Yang, S. X., and Luo, C., 2004, “A neural network approach to complete coverage path planning”, IEEE Transactions on Systems, Man and Cybernetics, Part B, 34(1), 718-724. https://doi.org/10.1109/TSMCB.2003.811769
  8. LaValle, S. M., 2006, “Planning Algorithms”, Cambridge University Press.
  9. Latombe, J. C., 1993, “Robot Motion Planning”, Boston, MA, Kluwer Academic Publishers.

피인용 문헌

  1. Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots vol.28, pp.5, 2011, https://doi.org/10.1002/rob.20403
  2. Aerial coverage optimization in precision agriculture management: A musical harmony inspired approach vol.99, 2013, https://doi.org/10.1016/j.compag.2013.09.008
  3. Optimal pest management by networked unmanned cropdusters in precision agriculture: A cyber-physical system approach vol.46, pp.30, 2013, https://doi.org/10.3182/20131120-3-FR-4045.00019
  4. Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey vol.72, pp.4, 2018, https://doi.org/10.1002/net.21818