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.

Abstract
This paper presents a set of novel modifications that can be applied to any grid-based path planning algorithm from the A* family used in mobile robotics. Five modifications are presented regarding the way the robot sees an obstacle and its target to plan the robot's path. The modifications make it possible for the robot to get to the target faster than traditional algorithms, as well as to avoid obstacles that move as fast as (or even faster than) the robot. Some simulations were made using a crowded and highly dynamic environment with twelve randomly moving obstacles. In these first simulations, a middle sized 5DPO robot was used. Also, real experiments were made with a small-sized version of a 5DPO robot to validate the algorithm's effectiveness. In all simulations and real robot experiments the objects are considered to be moving at a constant speed. Finally, we present an overall discussion and conclusion of this paper. © 2012 The Brazilian Computer Society.

Abstract
This paper presents a tracking system developed to study the behavior of an oil tanker moored at the Berth ‘‘A’’ of the Leixões Oil Terminal, Porto, Portugal. A brief description of the local environmental conditions and the existing operational conditions at that oil terminal are presented. Due to extreme outdoor working conditions a Kalman filter was implemented to ensure the robustness and reliability of the obtained measurements. Tests were performed in laboratory on a physical model of a moored oil tanker at a scale 1/100. The results were compared with a commercial motion capture system installed in laboratory. The presented measurement system was developed as part of the DOLPHIN project that aims to study the behavior of moored ships in harbors. © Springer International Publishing Switzerland 2013.

Abstract
Flexible and self-adaptive behaviours in automated quality control systems are features that may significantly enhance the robustness, efficiency and flexibility of the industrial production processes. However, most current approaches on automated quality control are based on rigid inspection methods and are not capable of accommodating to disturbances affecting the image acquisition quality, fact that hast direct consequences on the system's reliability and performance. In an effort to address the problem, this paper presents the development of a self-adaptive software system designed for the pre-processing (quality enhancement) of digital images captured in industrial production lines. The approach introduces the use of scene recognition as a key-feature to allow the execution of customized image pre-processing strategies, increase the system's flexibility and enable self-adapting conducts. Real images captured in a washing machines production line are presented to test and validate the system performance. Experimental results demonstrate significant image quality enhancements and a valuable reliability improvement of the automated quality control procedures.