Kinematic Stability based AFG-RRT Path Planning for Cable-Driven Parallel Robots

Abstract

Motion planning for Cable-Driven Parallel Robots (CDPRs) is a challenging task due to various restrictions on cable tensions, collisions and obstacle avoidance. The presented work aims at proposing an optimal path planning strategy in order to both maximize the wrench capability and the dexterity of the robot in a cluttered environment. First, an asymptoticallyoptimal path finding method based on a variant of rapidly exploring random trees (RRT) is implemented along with the GilbertJohnsonKeerthi (GJK) algorithm to account for the collision detections. Then, a goal biased Artificial Field Guide (AFG) is employed to reduce convergence time and ensure directional exploration. Finally, a post-processing algorithm is added to get a short and smooth resultant path by fitting appropriate splines. The proposed path planning strategy is analyzed and demonstrated on a simulated and experimental setup of a six-DOF spatial CDPR.