An optimal motion planning method of 7-DOF robotic arm for upper limb movement assistance

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.