Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Temporal Waypoint Revision Method to Solve Path Mismatch Problem of Hierarchical Integrated Path Planning for Mobile Vehicle

이동 차량의 계층적 통합 경로 계획의 경로 부조화 문제 해결을 위한 임시 경유점 수정법

  • Lee, Joon-Woo (Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Seok, Joon-Hong (Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Ha, Jung-Su (Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Lee, Ju-Jang (Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Lee, Ho-Joo (Agency for Defense Development (ADD))
  • Received : 2012.04.30
  • Accepted : 2012.06.20
  • Published : 2012.07.01
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Abstract

Hierarchical IPP (Integrated Path Planning) combining the GPP (Global Path Planner) and the LPP (Local Path Planner) is interesting the researches who study about the mobile vehicle in recent years. However, in this study, there is the path mismatch problem caused by the difference in the map information available to both path planners. If ever a part of the path that was found by the GPP is available to mobile vehicle, the part may be unavailable when the mobile vehicle generates the local path with its built-in sensors while the vehicle moves. This paper proposed the TWR (Temporal Waypoint Reviser) to solve the path mismatch problem of the hierarchical IPP. The results of simulation provide the performance of the IPP with the TWR by comparing with other path planners.

Keywords

References

  1. Y.-W. Lim, J.-S. Jeong, J.-U. An, and D.-H. Kim, "Path planning algorithm for UGVs based on the edge detecting and limit-cycle navigation method," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 5, pp. 471-478, 2011. https://doi.org/10.5302/J.ICROS.2011.17.5.471
  2. J.-H. Im, S.-H. You, G.-I. Jee, and D.-H. Lee, "A path generation algorithm for obstacle avoidance in waypoint navigation of unmanned ground vehicle," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 8, pp. 843-850, 2011. https://doi.org/10.5302/J.ICROS.2011.17.8.843
  3. A. Howard, H. Seraji, and B. Werger, "Global and regional path planners for integrated planning and navigation," Journal of Robotic Systems, vol. 22, no. 12, pp. 767-778, 2005. https://doi.org/10.1002/rob.20098
  4. A. Kelly, A. Stentz, O. Amidi, M. Bode, D. Bradley, and A. Diaz-Calderon, et al., "Toward reliable off road autonomous vehicles operating in challenging environments," The International Journal of Robotics Research, vol. 25, no. 5-6, pp. 449-483, 2006. https://doi.org/10.1177/0278364906065543
  5. N. Nilsson, Problem-solving methods in artificial intelligence, McGraw-Hill, 1971.
  6. A. Stentz, "The focussed D* algorithm for real-time replanning," Proc. of the International Joint Conference on Artificial Intelligence, pp. 1652-1659, 1995.
  7. S. Koenig and M. Likhachev, "D* Lite," Proc. of the National Conference on Artificial Intelligence, pp. 476-483, 2002.
  8. M. Seder, P. Mostarac, and I. Petrovic, "Hierarchical path planning of mobile robots in complex indoor environments," Transactions of the Institute of Measurement and Control, vol. 33, no. 3-4, pp. 332-358, 2011. https://doi.org/10.1177/0142331208100107
  9. K. W. Kwak, H. K. Jeong, T. S. Choe, Y. Park, Y. K. Kwak, and S. Kim, "an optimal path generation method considering the safe maneuvering of UGV," Journal of the Korea Institute of Military Science and Technology (in Korean), vol. 13, no. 6, pp. 951-957, 2010.

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