로봇학회논문지 (The Journal of Korea Robotics Society)

  • 제16권3호
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  • Pages.250-259
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  • 2021
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  • 1975-6291(pISSN)
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  • 2287-3961(eISSN)
한국로봇학회 (Korea Robotics Society)
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자율주차 상황에서 차량 구속 조건 고려에 따른 경로 계획 및 추종 성능의 비교 분석

A Comparative Analysis of Path Planning and Tracking Performance According to the Consideration of Vehicle's Constraints in Automated Parking Situations

  • Kim, Minsoo (Graduate School of Convergence Science and Technology, Seoul National University) ;
  • Ahn, Joonwoo (Graduate School of Convergence Science and Technology, Seoul National University) ;
  • Kim, Minsung (Graduate School of Convergence Science and Technology, Seoul National University) ;
  • Shin, Minyong (Phantom AI Inc.) ;
  • Park, Jaeheung (Graduate School of Convergence Science and Technology, Seoul National University, Advanced Institutes of Convergence Science and Technology)
  • 투고 : 2021.03.04
  • 심사 : 2021.05.10
  • 발행 : 2021.08.31
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초록

Path planning is one of the important technologies for automated parking. It requires to plan a collision-free path considering the vehicle's kinematic constraints such as minimum turning radius or steering velocity. In a complex parking lot, Rapidly-exploring Random Tree* (RRT*) can be used for planning a parking path, and Reeds-Shepp or Hybrid Curvature can be applied as a tree-extension method to consider the vehicle's constraints. In this case, each of these methods may affect the computation time of planning the parking path, path-tracking error, and parking success rate. Therefore, in this study, we conduct comparative analysis of two tree-extension functions: Reeds-Shepp (RS) and Hybrid Curvature (HC), and show that HC is a more appropriate tree-extension function for parking path planning. The differences between the two functions are introduced, and their performances are compared by applying them with RRT*. They are tested at various parking scenarios in simulation, and their advantages and disadvantages are discussed by computation time, cross-track error while tracking the path, parking success rate, and alignment error at the target parking spot. These results show that HC generates the parking path that an autonomous vehicle can track without collisions and HC allows the vehicle to park with lower alignment error than those of RS.

키워드

과제정보

This project was funded by Phantom AI Inc., and is currently supported by the publication grant

참고문헌

  1. M. Kim, G. Im, and J. Park, "A Comparative Study of Parking Path Following Methods for Autonomous Parking System," The Journal of Korea Robotics Society, vol. 15, no. 2, pp. 147-159, Jun., 2020, DOI: 10.7746/jkros.2020.15.2.147.
  2. P. Zips, M. Bock, and A. Kugi, "Optimisation based path planning for car parking in narrow environments," Robotics and Autonomous Systems, vol. 79, pp. 1-11, 2016, DOI: 10.1016/j.robot.2016.02.004.
  3. S. Zhang, Y. Chen, S. Chen, and N. Zheng, "Hybrid A*-based curvature continuous path planning in complex dynamic environments," 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 2019, DOI: 10.1109/ITSC.2019.8916953.
  4. S. Shin, J. Ahn, and J. Park, "Desired orientation rrt (do-rrt) for autonomous vehicle in narrow cluttered spaces," 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea (South), 2016, DOI: 10.1109/IROS.2016.7759696.
  5. Y. Dong, Y. Zhong, and J. Hong, "Knowledge-Biased Sampling-Based Path Planning for Automated Vehicles Parking," IEEE Access, vol. 8, pp. 156818-156827, 2020, DOI: 10.1109/ACCESS.2020.3018731.
  6. J. A. Reeds and L.A. Shepp, "Optimal paths for a car that goes both forwards and backwards," Pacific Journal of Mathematics, vol. 145, no. 2, pp. 367-393,1990, DOI: 10.2140/pjm.1990.145.367.
  7. T. Fraichard and A. Scheuer, "From Reeds and Shepp's to continuous-curvature paths," IEEE Transactions on Robotics, vol. 20, no. 6, pp. 1025-1035, 2004, DOI: 10.1109/TRO.2004.833789.
  8. H. Banzhaf, L. Palmieri,D. Nienhuser,T. Schamm, S. Knoop, and H. M. Zollner, "Hybrid curvature steer: A novel extend function for sampling-based nonholonomic motion planning in tight environments," 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). Yokohama, Japan, 2017, DOI: 10.1109/ITSC.2017.8317757.
  9. A. Scheuer and T. Fraichard, "Continuous-curvature path planning for car-like vehicles," 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97, Grenoble, France, 1997, DOI: 10.1109/IROS.1997.655130.
  10. A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, "CARLA: An open urban driving simulator," Machine Learning Research, 2017, [Online], http://proceedings.mlr.press/v78/dosovitskiy17a.html.
  11. J. D. Gammell, S. S. Siddhartha, and T. D. Barfoot, "Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic," 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA, 2014, DOI: 10.1109/IROS.2014.6942976.
  12. Y. Kanayama, Y. Kimura, F. Miyazaki, and T. Noguchi, "A stable tracking control method for an autonomous mobile robot," IEEE International Conference on Robotics and Automation, Cincinnati, OH, USA, 1990, DOI: 10.1109/ROBOT.1990.126006.