한국군사과학기술학회지 (Journal of the Korea Institute of Military Science and Technology)

  • 제20권5호
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  • Pages.647-653
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  • 2017
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  • 1598-9127(pISSN)
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  • 2636-0640(eISSN)
한국군사과학기술학회 (The Korea Institute of Military Science and Technology)
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비평지 및 먼지 환경에서 1차원 가상힘장 알고리즘의 실험적 검증

Experimental Verification of 1D Virtual Force Field Algorithm on Uneven and Dusty Environment

  • 최덕선 (국방과학연구소 제5기술연구본부) ;
  • 주상현 (국방과학연구소 제5기술연구본부) ;
  • 박용운 (국방과학연구소 제5기술연구본부) ;
  • 박진배 (연세대학교 전기전자공학과)
  • Choe, Tok Son (The 5th Research and Development Institute, Agency for Defense Development) ;
  • Joo, Sang-Hyun (The 5th Research and Development Institute, Agency for Defense Development) ;
  • Park, Yong-Woon (The 5th Research and Development Institute, Agency for Defense Development) ;
  • Park, Jin-Bae (Department of Electrical and Electronic Engineering, Yonsei University)
  • 투고 : 2017.02.03
  • 심사 : 2017.09.22
  • 발행 : 2017.10.05
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초록

In this paper, we deal with the experimental verification of 1D virtual force field algorithm based reflexive local path planning on uneven and dusty environment. The existing obstacle detection method on uneven and dusty environment and 1D virtual force field based reflexive local path planning algorithm simply are introduced. Although the 1D virtual force field algorithm is verified by various simulations, additional efforts are needed to verify this algorithm in the real-world. The introduced methods are combined with each other, installed to real mobile platforms and verified by various real experiments.

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참고문헌

  1. K. Bimbraw, "Autonomous Cars: Past, Present and Future a Review of the Developments in the Last Century, the Present Scenario and the Expected Future of Autonomous Vehicle Technology," 12th International Conference on Informatics in Control, Automation and Robotics, Vol. 1, pp. 191-198, 2015.
  2. M. Hebert, C. Thorpe, and A. Stentz, "Intelligent Unmanned Ground Vehicles: Autonomous Navigation Research at Carnegie Mellon," Springer Science and Business Media, 2012.
  3. C. Berger and B. Rumpe, "Autonomous Driving-5 Years After the Urban Challenge: The Anticipatory Vehicle as a Cyber-Physical System," 10th Workshop on Automotive Software Engineering, pp. 789-798, September 2012.
  4. J. Markoff, "At High Speed, on the Road to a Driverless Future," New York Times, 2013.
  5. Zhang, T., "UGV Developments in 2020-2030 in Terms of Technologies," Digital Infantry Battefield Solution, 2016.
  6. T. Choe, J. Park, S. Joo, and Y. Park, “1D Virtual Force Field Algorithm for Reflexive Local Path Planning of Mobile Robots,” Electronics Letters, Vol. 50, No. 20, pp. 1429-1430, 2014. https://doi.org/10.1049/el.2014.2787
  7. T. Choe, J. Park, S. Joo, and Y. Park., "Obstacle Detection for Unmanned Ground Vehicle on Uneven and Dusty Environment," Proc. SPIE 9474, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, 94741D, May 21, 2015.
  8. Koren, Y. and Borenstein, J., "Potential Field Methods and Their Limitations for Mobile Robot Navigation", IEEE International Conference on Robotics and Automation, pp. 1398-1404, April 1991.
  9. Veelaert, P. and Bogaerts, W., “Ultrasonic Potential Field Sensor for Obstacle Avoidance,” IEEE transactions on Robotics and Automation, Vol. 15, No. 4, pp. 774-779, 1999. https://doi.org/10.1109/70.782033
  10. Kim, D. H. and Shin, S., “Local Path Planning using a New Artificial Potential Function Composition and its Analytical Design Guidelines,” Advanced Robotics, Vol. 20, No. 1, pp. 115-135, 2006. https://doi.org/10.1163/156855306775275530
  11. J. Borenstein and Y. Koren, “The Vector Field Histogram - Fast Obstacle Avoidance for Mobile Robots,” IEEE Transactions on Robotics and Automation, Vol. 7, No. 3, pp. 278-288, 1991. https://doi.org/10.1109/70.88137
  12. Ulrich, I, and Borenstein, J., "VFH*: Local Obstacle Avoidance with Look-Ahead Verification," International Conference on Robotics and Automation, Vol. 3, pp. 572-577, 24-28 April 2000.
  13. Kazem, B. I., Hamad, A. H. and Mozael, M. M., “Modified Vector Field Histogram with a Neural Network Learning Model for Mobile Robot Path Planning and Obstacle Avoidance,” International Journal of Advanced Computer Technology, Vol. 2, No. 5, pp. 166-173, 2010. https://doi.org/10.4156/ijact.vol2.issue5.18
  14. Moghadam, P., Wijesoma, W. S. and Feng, D. J., "Improving Path Planning and Mapping based on Stereo Vision and Lidar," IEEE 10th International Conference on Control, Automation, Robotics and Vision, pp. 384-389, Dec. 2008.
  15. Manz, M., Himmelsbach M., Luettel T. and Wuensche, H. J., "Detection and Tracking of Road Networks in Rural Terrain by Fusing Vision and Lidar," International Conference on Intelligent Robots and Systems, pp. 4562-4568, 2011.
  16. Shang, E., An, X., Li, J. and He, H., "A Novel Setup Method of 3D Lidar for Negative Obstacle Detection in Field Environment," International Conference on Intelligent Transportation Systems, pp. 1436-1441, 2014.