TY - GEN
T1 - Informed RRT∗ towards optimality by reducing size of hyperellipsoid
AU - Kim, Min Cheol
AU - Song, Jae Bok
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/25
Y1 - 2015/8/25
N2 - Wrapping-based informed RRT∗ is a modified version of informed RRT∗. Informed RRT∗ formulates an n-dimensional hyperellipsoid from which it generates new sample nodes. This has a dramatically increased chance of sampling nodes that will improve the current best solution compared to conventional RRT∗. However, due to explorative and randomized behaviors of RRT∗, the size of the hyperellipsoid will unlikely be small enough to call it effective. To solve this matter, wrapping-based informed RRT∗ proposed in this paper combines a size-diminishing procedure called 'wrapping process' with informed RRT∗. The proposed planner can advance from the first solution acquired by the planner to the improved, feasible solution which can drastically reduce the size of the hyperellipsoid. Therefore, the required time consumption in order to acquire the globally optimal solution is reduced dramatically. The algorithm was tested in various environments with different numbers of joint variables and showed much better performance than the existing planners. Furthermore, the wrapping process proved to be a comparably insignificant computational burden regardless of the number of dimensions of the configuration space.
AB - Wrapping-based informed RRT∗ is a modified version of informed RRT∗. Informed RRT∗ formulates an n-dimensional hyperellipsoid from which it generates new sample nodes. This has a dramatically increased chance of sampling nodes that will improve the current best solution compared to conventional RRT∗. However, due to explorative and randomized behaviors of RRT∗, the size of the hyperellipsoid will unlikely be small enough to call it effective. To solve this matter, wrapping-based informed RRT∗ proposed in this paper combines a size-diminishing procedure called 'wrapping process' with informed RRT∗. The proposed planner can advance from the first solution acquired by the planner to the improved, feasible solution which can drastically reduce the size of the hyperellipsoid. Therefore, the required time consumption in order to acquire the globally optimal solution is reduced dramatically. The algorithm was tested in various environments with different numbers of joint variables and showed much better performance than the existing planners. Furthermore, the wrapping process proved to be a comparably insignificant computational burden regardless of the number of dimensions of the configuration space.
KW - RRT
KW - RRT
KW - Sampling-based motion planning
KW - optimal motion planning
UR - http://www.scopus.com/inward/record.url?scp=84951080848&partnerID=8YFLogxK
U2 - 10.1109/AIM.2015.7222539
DO - 10.1109/AIM.2015.7222539
M3 - Conference contribution
AN - SCOPUS:84951080848
T3 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
SP - 244
EP - 248
BT - AIM 2015 - 2015 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015
Y2 - 7 July 2015 through 11 July 2015
ER -