Automatized Switchgear Wiring: An Outline of the WIRES Experiment Results

This chapter reports an overview of the experience and the results achieved during the development of the robotized system for switchgear wiring carried out in the WIRES experiment. This specific application is particularly challenging for a robotic system due to the complexity of the manipulation task. As a matter of fact, in this task deformable linear objects, such as electric wires, are involved. Moreover, the precision requested during the assembly task and the typical crowded space inside the switchgear imply, on one side, the development of specific hardware and software tools and, on the other side, high adaptability and flexibility of the robotic system. In the WIRES experiment, a software package to extract the wiring information and to generate the robot task sequence directly from the switchgear CAD files has been developed. Additionally, a computer vision system able to recognize the location of the wires and of the electromechanical components inside the switchgear has been developed. To deal with the wire deformability and occlusion problems during the wire insertion, machine learning and sensor fusion techniques have been adopted to enable the wire manipulation by means of tactile sensors and 2D cameras feedback. From the hardware point of view, a specific end effector has been developed to manipulate and connect the wire to the components. This end effector is equipped with an electric screwdriver and a customized tactile sensor used to evaluate the wire shape, the wire end pose and its interaction with the environment during the manipulation. The entire task pipeline, going from the switchgear information extraction, to the wire grasp and manipulation, its connection and the routing along the desired wire path is presented in this chapter. The preliminary experimental results show that the developed system can achieve a success rate of about 95% in the wire insertion and connection task.