JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Improvement of Online Motion Planning based on RRT* by Modification of the Sampling Method
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Improvement of Online Motion Planning based on RRT* by Modification of the Sampling Method
Lee, Hee Beom; Kwak, HwyKuen; Kim, JoonWon; Lee, ChoonWoo; Kim, H.Jin;
 
 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 () 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 which is an extended version of to increase the rate of convergence to optimal solution by improving the sampling method of . 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 by combining with the sampling method to improve the path nearby robot. With comparison among basic , informed and the proposed in online motion planning, the proposed showed the best result by representing the closest solution to optimum.
 Keywords
online motion planning;; sampling method;
 Language
Korean
 Cited by
 References
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

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. crossref(new window)

3.
S. M. LaValle, "Rapidly-Exploring Random Trees A New Tool for Path Planning," 1998.

4.
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. crossref(new window)

5.
Karaman, Sertac, and Emilio Frazzoli, "Incremental sampling-based algorithms for optimal motion planning," arXiv preprint arXiv:1005.0416, May 2010.

6.
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.

7.
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.

8.
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.

9.
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.