Moving obstacle avoidance for cable-driven parallel robots using improved RRT

Abstract

Moving obstacle avoidance is one of the most challenging problems for cable-driven parallel robots (CDPRs) due to various constraints. In this work, the improved rapidly exploring random tree (RRT) method is proposed to address moving obstacle avoidance for CDPRs. Compared with the conventional RRT method which mainly focused on the static environment, the suggested method is goal-biased with dynamic step size makes it possible to implement in a dynamic environment. To deal with the particularity of CDPRs, the improved RRT method considers various constraints caused by the cable particularity, which include cable interference and the twist feasible workspace. The swept volume of CDPRs is considered to convert the spatiotemporal problem into a pure spatial problem, therefore the Gilbert–Johnson–Keerthi algorithm was applied to collision detection. Additionally, the velocity information is utilized to estimate time and distance of the closest approach, which also prevents the local minima. The simulation is conducted to illustrate the suggested method and the simulation results are compared with our previous APG-RRT method using batch evaluation. According to the simulation results, the suggested method finds an optimized collision-free path with the average path cost reduced by 32%, and the oscillation time is reduced by 36%. Finally, the suggested method is verified by the experiment.