TY - GEN
T1 - An optimal motion planning method of 7-DOF robotic arm for upper limb movement assistance
AU - Liu, Zemin
AU - Ai, Qingsong
AU - Liu, Yaojie
AU - Zuo, Jie
AU - Zhang, Xiong
AU - Meng, Wei
AU - Xie, Shane
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Assistive robotic arm is crucial alternative resource for people disabled or injured in the upper limbs, which enable them to complete basic living tasks independently. Thus, an extremely accurate motion planning for robotic arm needs to be applied to improve assistive performance. Rapidly-Exploring Random Tree Star (RRT*) is one of the most representative methods, however, this method has great limitations due to the tedious iteration process while planning. In this study, the potentials guide sampling based-on RRT∗ (PGS-RRT*) approach is introduced through combination with artificial potential fields (APF) as an expansion of RRT∗ algorithm. A revision of repulsive potential force's formula in APF has been applied into sampling process of RRT*. The samples during motion planning is gathered through the optimization of potentials formulations. Specifically, the basic potential function give each sample an offset oriented to goal. Experiments in 2D and 3D environments and simulations on KUKA LBR iiwa 7 prove that the PGS-RRT∗ method is able to find an optimal path in a short time, which highlights the application prospect on robots with a number of degree of freedom (DOF) in patient's daily life assistance.
AB - Assistive robotic arm is crucial alternative resource for people disabled or injured in the upper limbs, which enable them to complete basic living tasks independently. Thus, an extremely accurate motion planning for robotic arm needs to be applied to improve assistive performance. Rapidly-Exploring Random Tree Star (RRT*) is one of the most representative methods, however, this method has great limitations due to the tedious iteration process while planning. In this study, the potentials guide sampling based-on RRT∗ (PGS-RRT*) approach is introduced through combination with artificial potential fields (APF) as an expansion of RRT∗ algorithm. A revision of repulsive potential force's formula in APF has been applied into sampling process of RRT*. The samples during motion planning is gathered through the optimization of potentials formulations. Specifically, the basic potential function give each sample an offset oriented to goal. Experiments in 2D and 3D environments and simulations on KUKA LBR iiwa 7 prove that the PGS-RRT∗ method is able to find an optimal path in a short time, which highlights the application prospect on robots with a number of degree of freedom (DOF) in patient's daily life assistance.
UR - https://www.scopus.com/pages/publications/85074283220
U2 - 10.1109/AIM.2019.8868594
DO - 10.1109/AIM.2019.8868594
M3 - 会议稿件
AN - SCOPUS:85074283220
T3 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
SP - 277
EP - 282
BT - Proceedings of the 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2019
Y2 - 8 July 2019 through 12 July 2019
ER -