A RRT-A* Path Planning Algorithm for Cable-driven Manipulators

Abstract

Cable-driven manipulators (CDMs) are widely used for operations in confined spaces due to their slender bodies and multiple degrees of freedom (DOFs). To plan passable paths for them in narrow spaces, a rapidly exploring random tree (RRT) algorithm is often used. However, the cost of planning process are not considered in this method. In order to improve the quality of path planning of CDMs, this work optimizes a traditional RRT algorithm by fusing it with an A* algorithm. In the novel RRT-A* method, the RRT algorithm is used to generate feasible paths, the A* algorithm is used to estimate the cost and measure the selection of the traversal search of each feasible node of the path. Compared with the traditional RRT algorithm, the novel algorithm is better in some performances such as complex path, path redundancy and large random path angle. Simulation results show that this method can effectively reduce path cost and the number of nodes. For further validation, a 17 DOFs CDM prototype is conducted to move in multi-obstacle environments to test the proposed method.