Browse > Article
http://dx.doi.org/10.5302/J.ICROS.2016.15.0135

Improvement of Online Motion Planning based on RRT* by Modification of the Sampling Method  

Lee, Hee Beom (School of Mechanical and Aerospace, Seoul National University)
Kwak, HwyKuen (Command & Control Group, Hanwha Thales)
Kim, JoonWon (Command & Control Group, Hanwha Thales)
Lee, ChoonWoo (Command & Control Group, Hanwha Thales)
Kim, H.Jin (School of Mechanical and Aerospace, Seoul National University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.22, no.3, 2016 , pp. 192-198 More about this Journal
Abstract
Motion planning problem is still one of the important issues in robotic applications. In many real-time motion planning problems, it is advisable to find a feasible solution quickly and improve the found solution toward the optimal one before the previously-arranged motion plan ends. For such reasons, sampling-based approaches are becoming popular for real-time application. Especially the use of a rapidly exploring random $tree^*$ ($RRT^*$) algorithm is attractive in real-time application, because it is possible to approach an optimal solution by iterating itself. This paper presents a modified version of informed $RRT^*$ which is an extended version of $RRT^*$ to increase the rate of convergence to optimal solution by improving the sampling method of $RRT^*$. In online motion planning, the robot plans a path while simultaneously moving along the planned path. Therefore, the part of the path near the robot is less likely to be sampled extensively. For a better solution in online motion planning, we modified the sampling method of informed $RRT^*$ by combining with the sampling method to improve the path nearby robot. With comparison among basic $RRT^*$, informed $RRT^*$ and the proposed $RRT^*$ in online motion planning, the proposed $RRT^*$ showed the best result by representing the closest solution to optimum.
Keywords
online motion planning; $RRT^*$; $RRT^*$ sampling method;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 G.-Y. Song and J.-W. Lee, "Path planning for autonomous navigation of a driverless ground vehicle based on waypoints," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 20, no. 2, pp. 211-217, Feb. 2014   DOI
2 L. E. Kavraki, P. Švestka, J.-C. Latombe, and M. H. Overmars, "Probabilistic roadmaps for path planning in high-dimensional configuration spaces," Robotics and Automation, IEEE Transactions on, vol. 12, no. 4, pp. 566-580, Aug. 1996.   DOI
3 Y. Kuwata, S. Karaman, J. Teo, E. Frazzoli, J. P. How, and G. Fiore, "Real-time motion planning with applications to autonomous urban driving," Control Systems Technology, IEEE Transactions on, vol. 17, no. 5, pp. 1105-1118, Sep. 2009.   DOI
4 Karaman, Sertac, and Emilio Frazzoli, "Incremental sampling-based algorithms for optimal motion planning," arXiv preprint arXiv:1005.0416, May 2010.
5 D. Lee and D. H. Shim, "Optimal path planner considering real terrain for fixed-wing UAVs," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 20, no. 12, pp. 1272-1277, Dec. 2014.   DOI
6 S. Karaman, M. R. Walter, A. Perez, E. Frazzoli, and S. Teller, "Anytime motion planning using the RRT*," Robotics and Automation (ICRA), 2011 IEEE International Conference on. IEEE, pp. 1478-1483, May 2011.
7 F. Islam, J. Nasir, U. Malik, Y. Ayaz, and O. Hasan, "RRT∗-smart: Rapid convergence implementation of rrt ∗ towards optimal solution," Mechatronics and Automation (ICMA), 2012 International Conference on. IEEE, pp. 1651-1656, Aug. 2012.
8 J. D. Gammell, S. S. Srinivasa, 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 (IROS 2014), pp. 2997-3004, Sep. 2014.
9 S. M. LaValle, "Rapidly-Exploring Random Trees A New Tool for Path Planning," 1998.