Abstract
This paper describes the EMG30 mechanical and electrical modeling and its simulation resorting to SimTwo (Robot@Factory mobile robot competition official simulator). It is described the developed setup applied to obtain the experimental data that was used to estimate the actuator parameters. It was obtained an electro-mechanical dynamical model that describes the motor, its gear box, and the encoder. The motivation to model and simulate the EMG30 is the fact that it is an actuator worldwide popular in the mobile robotics domain, being a low cost 12v motor equipped with encoders and a 30:1 reduction gearbox. The Goal of this work is to provide more realism and new features to the Robot@Factory official simulator, allowing participating teams to produce and validate different robot prototypes and its software, reducing considerably the development time. © Springer International Publishing Switzerland 2013.

Abstract
Industrial laser cutting machines use a type of support base that sometimes causes the cut metal parts to tilt or fall, which hinders the robot from picking the parts after cutting. The objective of this work is to calculate the 3D orientation of these metal parts with relation to the main metal sheet to successfully perform the subsequent robotic pick-and-place operation. For the perception part the system relies on the low cost 3D sensing Microsoft Kinect, which is responsible for mapping the environment. The previously known part positions are mapped in the new environment and then a plane fitting algorithm is applied to obtain its 3D orientation. The implemented algorithm is able to detect if the piece has fallen or not. If not, the algorithm calculates the orientation of each piece separately. This information is later used for the robot manipulator to perform the pick-and-place operation with the correct tool orientation. This makes it possible to automate a manufacturing process that is entirely human dependent nowadays.

Abstract

Abstract
Cooperation with humans is a requirement for the next generation of robots so it is necessary to model how robots can sense, know, share and acquire knowledge from human interaction. Instead of traditional SLAM (Simultaneous Localization and Mapping) methods, which do not interpret sensor information other than at the geometric level, these capabilities require an environment map representation similar to the human representation. Topological maps are one option to translate these geometric maps into a more abstract representation of the the world and to make the robot knowledge closer to the human perception. In this paper is presented a novel approach to translate 3D grid map into a topological map. This approach was optimized to obtain similar results to those obtained when the task is performed by a human. Also, a novel feature of this approach is the augmentation of topological map with features such as walls and doors.

Abstract
This paper presents a framework for robot programming by demonstration using gesture. It is based on a luminous multi-LED marker which is captured by a pair of industrial cameras. Using stereoscopy the marker supplies a complete 6-DoF human gesture tracking output with both position and orientation. Tests show that the developed setup is industrial grade, being precise for many industrial applications and robust particularly to lighting conditions. Attaching the marker to an operator work tool provides an efficient way to track the human movements without further intrusion in the process. The resulting path is used to generate a program for an industrial manipulator ending the cycle in an human-robot skill transfer framework.

Abstract
This paper presents a novel localization method for small mobile robots. The proposed technique is especially designed for the Robot@Factory which is a new robotic competition presented in Lisbon 2011. The real-time localization technique resorts to low-cost infra-red sensors, a map-matching method and an Extended Kalman Filter (EKF) to create a pose tracking system that is well-behaved. The sensor information is continuously updated in time and space through the expected motion of the robot. Then, the information is incorporated into the map-matching optimization in order to increase the amount of sensor information that is available at each moment. In addition, a particle filter based on Particle Swarm Optimization (PSO) relocates the robot when the map-matching error is high. Meaning that the map-matching is unreliable and robot is lost. The experiments conducted in this paper prove the ability and accuracy of the presented technique to localize small mobile robots for this competition. Therefore, extensive results show that the proposed method have an interesting localization capability for robots equipped with a limited amount of sensors.