Experimental study on the kinematic control of a cable suspended parallel robot for object tracking purpose

In this paper, two novel methods are proposed and compared which allows computation of the position of the end-effector in under-constrained cable-driven parallel robots. In the first method, from the data collected from an inertial measurement unit attached on the end-effector, the forward kinematic problem is reduced to a linear system of equations in which the position of the end-effector can be readily obtained real-time which is a definite asset for control purpose. In what concern the second method, a LoLiMoT neural network is trained with data collected from the simulation environment to solve the forward kinematic problem. Based on the obtained position feedback, an experimental closed loop kinematic control is performed and the proposed method is validated based on the observation of a camera which is mounted on the end-effector. A simple low cost mechanical structure is designed and constructed to measure the cable tensions by use of bending Loadcells. The sensors were calibrated and their data were used to guarantee the existence of minimum force in cables during the end-effector's motion. At last, an algorithm for tracking a moving object is proposed and implemented. The experimental results verify the efficiency of the proposed methods.