José Alexandre Gonçalves

Abstract
Throughout this paper, the model, its parameter estimation and a controller for a solution using a DC motor with a gearbox worm, coupled to a non-rigid joint, will be presented. First, the modeling of a non-linear system based on a DC Motor with Worm Gearbox coupled to a non-rigid joint is presented. The full system was modeled based on the modeling of two sub-systems that compose it - a non-rigid joint configuration and the DC motor with the worm gearbox configuration. Despite the subsystems are interdependent, its modelling can be performed independently trough a carefully chosen set of experiments. Modeling accurately the system is crucial in order to simulate and know the expected performance. The estimation process and the proposed experimental setup are presented. This setup collects data from an absolute encoder, a load cell, voltage and current sensors. The data obtained from these sensors is presented and used to obtaining some physical parameters from both systems. Finally, through an optimization process, the remaining parameters are estimated, thus obtaining a realistic model of the complete system. Finally, the controller setup is presented and the results obtained are also presented.

Abstract

Abstract
The Robot@Factory Lite (R@FL) is a competition held at the Portuguese Robotics Open that aims to present a problem inspired by the deployment of autonomous mobile robots on a factory shop floor. This paper proposes an approach to transform this competition according to the Industry 4.0 concept using the Wi-Fi to attribute orders to the mobile robot. The main contribution of this paper is to address a Supply Chain Management (SPM) of the ERP (Enterprise Resource Planning) that will inform the tasks to the robot so that it can schedule. It is presented a new hardware architecture that should be able to read the information of the parts through Wi-Fi in a client-server methodology. It also includes encoders that allow to feedback the wheels rotation and can be used to estimate the odometry. © 2020 IEEE.

Abstract

Abstract