Browse > Article
http://dx.doi.org/10.7746/jkros.2013.8.1.043

Path Planning for the Shortest Driving Time Considering UGV Driving Characteristic and Driving Time and Its Driving Algorithm  

Noh, Chi-Beom (Mechanical Engineering, Pusan National University)
Kim, Min-Ho (Mechanical Engineering, Pusan National University)
Lee, Min-Cheol (Mechanical Engineering, Pusan National University)
Publication Information
The Journal of Korea Robotics Society / v.8, no.1, 2013 , pp. 43-50 More about this Journal
Abstract
$A^*$ algorithm is a global path generation algorithm, and typically create a path using only the distance information. Therefore along the path, a moving vehicle is usually not be considered by driving characteristics. Deceleration at the corner is one of the driving characteristics of the vehicle. In this paper, considering this characteristic, a new evaluation function based path algorithm is proposed to decrease the number of driving path corner, in order to reduce the driving cost, such as driving time, fuel consumption and so on. Also the potential field method is applied for driving of UGV, which is robust against static and dynamic obstacle environment during following the generated path of the mobile robot under. The driving time and path following test was occurred by experiments based on a pseudo UGV, mobile robot in downscaled UGV's maximum and driving speed in corner. The experiment results were confirmed that the driving time by the proposed algorithm was decreased comparing with the results from $A^*$ algorithm.
Keywords
$A^*$ algorithm; Path Planning; Potential Field; Reduce drive cost;
Citations & Related Records
연도 인용수 순위
  • Reference
1 http://www.amitbhawani.com/blog/drive-without-driver-google-car/
2 http://en.wikipedia.org/wiki/DARPA_Grand_Challenge
3 J. H. Lee, E. H. Jung, B. C. Ko "Status and Prospects of Intelligent Autonomous vehicle contest" Korea Multimedia Society Vol. 14, No. 1. pp 31-41 March 2010
4 http://theory.stanford.edu/-amitp/GameProgramming/
5 O. Khatib, "Real-time Obstacle Avoidance for Manipulators and Mobile Robots," International Journal of Robotics Research, Vol. 5, No. 1, pp 90-98, 1986.
6 E. Rimon, and D. E. Koditschek, "Exact robot navigation using artificial potential functions" IEEE Trans. On Robotics and Automation, Vol. 9(5), 501-518, 1992.
7 M. G. Park and M. C. Lee,"Artificial Potential Field Based Path Planning for Mobile Robots Using a Virtual Obstacle Concept", 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, vol. 2, pp.20-24. July. 2003
8 Liu Chengqing, Marcelo H ang Jr, Hariharan Krishnan "Virtual Obstacle Concept for Local-minimum-recovery in Potential-field Based Navigation", 2000 IEEE International Conference on Robotics & Automation vol. 2, pp.983-988. April. 2000
9 M. H. Kim, Y. Wei, M. C. Lee " A Study of New Path Planning Algorithm using Extended A* Algorithm with Survivability" The 5th International Conference on Intelligent Robotics and Applications(ICIRA) October. 2012
10 http://arainyday.se/projects/python/AStar/
11 http://theory.stanford.edu/-amitp/GameProgramming/Heuri stics.html#S7