Abstract
This paper describes a humanoid robot simulator supporting joint trajectory optimization, following accurately the real robot characteristics. The simulator, based on a rigid body simulator (Open Dynamics Engine) and an OpenGL based graphics librmy (GLScene), provides instant visual feedback and realistic dynamics. It allows to design and test behaviours and control methods without access to the real hardware, preventing damages in the real robot in the earlier stages of development. Having in mind the energy consumption minimization, the low level trajectory planning is discussed and experimental results are presented. The proposed methods arc shown to minimize the total energy assuming two intervals of constant acceleration. Copyright © 2008 by World Scientific Publishing Co. Pte. Ltd.

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
Object localization and tracking is core to many practical applications, like human-computer interaction, security and surveillance, robot competitions and Industry 4.0. Such task may be computationally demanding, especially for traditional embedded systems, that usually have tight processing and storage constraints. This calls for the investigation of alternatives, including emergent heterogeneous embedded systems, like the Parallella line of single-board-computers (SBCs). The work presented in this paper explores the use of a Parallella board with a 16-core Epiphany co-processor, to perform real-time tracking of objects in frames captured by a Kinect sensor, based on color segmentation. We addressed several processing strategies, trying to assess which one performed better. We also ran the same code (where applicable) in several models of the Raspberry Pi platform, for comparison. We conclude that effectively exploring the Epiphany co-processor is not trivial, requiring considerable programming effort and suitable applications (CPU-demanding and highly parallelizable), to the extent that simpler development approaches, on more recent SBCs may be more effective. © 2017.

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.