Transactions of the Korean Society of Mechanical Engineers A (대한기계학회논문집A)

The Korean Society of Mechanical Engineers (대한기계학회)
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Design of Near-Minimum Time Path Planning Algorithm for Autonomous Driving

무인 자율 주행을 위한 최단 시간 경로계획 알고리즘 설계

  • Kim, Dongwook (School of Mechanical and Aerospace Engineering, Seoul Nat'l Univ.) ;
  • Kim, Hakgu (School of Mechanical and Aerospace Engineering, Seoul Nat'l Univ.) ;
  • Yi, Kyongsu (School of Mechanical and Aerospace Engineering, Seoul Nat'l Univ.)
  • 김동욱 (서울대학교 기계항공공학부) ;
  • 김학구 (서울대학교 기계항공공학부) ;
  • 이경수 (서울대학교 기계항공공학부)
  • Received : 2012.05.03
  • Accepted : 2013.02.04
  • Published : 2013.05.01
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Abstract

This paper presents a near-minimum time path planning algorithm for autonomous driving. The problem of near-minimum time path planning is an optimization problem in which it is necessary to take into account not only the geometry of the circuit but also the dynamics of the vehicle. The path planning algorithm consists of a candidate path generation and a velocity optimization algorithm. The candidate path generation algorithm calculates the compromises between the shortest path and the path that allows the highest speeds to be achieved. The velocity optimization algorithm calculates the lap time of each candidate considering the vehicle driving performance and tire friction limit. By using the calculated path and velocity of each candidate, we calculate the lap times and search for a near-minimum time path. The proposed algorithm was evaluated via computer simulation using CarSim and Matlab/Simulink.

본 논문은 무인 자율 주행을 위한 최소 시간 경로계획 알고리즘에 대해서 제안하였다. 최소 시간 경로계획 문제는 경로의 기하학적인 형상에 대한 고려뿐만이 아니라 차량 동역학까지 고려해야 하는 최적 문제이다. 경로계획은 후보 경로 생성 알고리즘과 속도 최적화 알고리즘으로 구성된다. 후보 경로 생성 알고리즘은 최단 거리 경로와 최고 속도 경로를 조합하여 후보경로를 생성한다. 속도 최적화 알고리즘은 차량의 주행성능 한계와 타이어 마찰 한계를 고려하여 각 후보 경로의 최고 속도를 계산한다. 이렇게 계산된 경로와 속도를 이용하여 각 후보 경로의 주행 시간을 계산하고 가장 작은 주행 시간의 경로를 최단시간 경로로 도출한다. 그리고 제안한 알고리즘은 CarSim 과 Matlab/Simulink 를 사용한 시뮬레이션을 통해 검증하였다.

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References

  1. Thrun, S., Montemerlo, M. and Dahlkamp, H., 2006, "Stanley, the Robot that Won the DARPA Grand Challenge," Journal of Field Robotics, Vol. 23, pp. 661-692.. https://doi.org/10.1002/rob.20147
  2. Cerri, P., Soprani, G. and Choi, J., 2011, "Computer Vision at the Hyundai Autonomous Challenge," Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on, pp. 777-783
  3. Yamakadoa, M., Takahashia, J. and Saitoa, S., "Improvement in Vehicle Agility and Stability by GVectoring Control," International Journal of Vehicle Mechanics and Mobility, Vol. 48, pp. 231-254
  4. Gustafsson, F., 1997, "Slip-Based Tire-Road Friction Estimation," Automatica, Vol. 33, pp. 1087-1099 https://doi.org/10.1016/S0005-1098(97)00003-4
  5. Cho, W., Yoon, J. and Yi, K., "Estimation of Tire Forces for Application to Vehicle Stability Control," IEEE Transations on Vehicular Technology, Vol. 59, pp. 638-649
  6. Villagra, J. and Milanes, V., 2012, "Path and Speed Planning for Smooth Autonomous Navigation" IEEE Intelligent Vehicles Symposium.
  7. Lini,G. and Piazzi, A., 2010, "Time-Optimal Dynamic Path Inversion for an Automatic Guided Vehicle," 49th IEEE Conference on Decision and Control.
  8. Braghin, F., Cheli, F., Melzi, S. and Sabbioni, E., 2008, "Race Driver Model," Computers and Structure 86, 1503-1516. https://doi.org/10.1016/j.compstruc.2007.04.028
  9. Kang, J. Y., 2007, Development of the Human Driver Model Based on the Human Factor, MS Thesis, Seoul National University.
  10. Kang, J. and Yi, K., 2006, "Development of a Finite Optimal Preview Control-Based Human Driver Steering Model," KSAE
  11. Burl, J. B., 1998, Linear Optimal Control, pp.179-226.
  12. Kim, H. and Yi, K., 2012, "Design of a Model Reference Cruise Control Algorithm," SAE2012 World Congress