A Cosine Similarity Based Multitarget Path Planning Algorithm for Cable-Driven Manipulators

Abstract

Many path planning algorithms are proposed and employed for cable-driven manipulators (CDMs). However, most of them only consider single-target-point tasks. For multitarget-point tasks, CDMs need to repeat the planning and following of single point tasks. This is feasible but not optimal in terms of the distance and time needed by CDMs to complete such tasks. To solve this problem, this work designs a novel two-stage multitarget-point path planning (MPP) method. In the first stage, an improved rapidly exploring random tree (RRT)-A* algorithm that considers CDMs' features is used to preplan passable paths between each target and a start point. In the second one, in order to avoid CDM's repetitively moving along similar preplanned paths, a cosine similarity theory is used, for the first time, to integrate these paths. Furthermore, an indicator named path cost is defined to evaluate paths. This indicator takes into account CDMs' constraints, paths' lengths, and energy consumption. Simulations are conducted to compare MPP with some classical and recently developed algorithms. The results shows that it well outperform them in terms of path length and tracking time. Furthermore, the proposed method is verified by experiments in a 17 degrees-of-freedom CDM prototype.