Abstract
This paper describes a robust localization system, similar to the used by the teams participating in the Robocup Small size league (SLL). The system, developed in Object Pascal, allows real time localization and control of an autonomous omnidirectional mobile robot. The localization algorithm is done resorting to odometry and global vision data fusion, applying an extended Kalman filter, being this method a standard approach for reducing the error in a least squares sense, using measurements from different sources.

Abstract
This paper's main purpose is to present an automatic vision assortment system for cork classification. Cork is a natural material that is used to seal wine bottles due to its reliability and its chemical and mechanic properties. A modular approach isolates the hard real-time sub problems. It includes some custom developed hardware and software. The developed and implemented system acquires images using a linear CCD camera, transmits that image over an Ethernet based network and processes that image to extract quantitative elements. This system can analyse and distinguish defects present on the surface of a cork-stopper and it is also possible to adapt it to other kinds of problems. The size, form and position of the defects are analysed and the stability of repeated measures is observed. This result allows us to validate the presented methodology.

Abstract
This paper describes the sensor and actuator modeling of a realistic wheeled mobile robot simulator The motivation of developing such simulator is to produce a personalized versatile tool that allows production and validation of robot software reducing considerably the development time. The mobile robot simulator was developed in Object Pascal with its dynamics based on the ODE (Open Dynamics Engine), allowing to develop robot software for a three wheel omnidirectional robot equipped with Infra-Red distance sensors and brushless motors.

Abstract
Recent developments in dexterous robotic manipulation technologies allowed for the design of very compact, yet capable, multi-fingered robotic hands. These can be designed to emulate the human touch and feel, reducing the aforementioned need for human expertise in highly detailed tasks. The presented work focused on the application of two simulation platforms Gazebo and MuJoCo - to a use-case of a Schunk Five Finger Robotic Hand, coupled to the UR5 collaborative manipulator. This allowed to assess the relative appropriateness of each of these platforms.

Abstract
The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.

Abstract
The robot presented in this paper was developed with the main focus on participating in robotic competitions. Therefore, the subsystems here presented were developed taking into account performance criteria instead of simplicity. Nonetheless, this paper also presents background knowledge in some basic concepts regarding robot localization, navigation, color identification and control, all of which are key for a more competitive robot.