Abstract
This paper presents MicroFactory - a simplified version of the Robot@Factory competition. This version of the competition was conceived to be low-cost and easily implementable in a small space, be it a classroom or the school robotics club. The factory scenario size was originally 3.5m by 2.5m. The floor is now an A0 printed sheet and the warehouses and machines dimensions are so that they can be 3D printed or made out of LEGO (TM) bricks. Both machines and parts had active elements with leds; now they are passive. Robot@Factory is a Portuguese robotic competition whose first edition was held in 2011 in Lisbon. The scenario of the competition simulates a factory which has an Incoming Warehouse, an Outgoing Warehouse, and 8 processing machines. The robots must collect, transport and position the materials, self-localize and navigate while avoiding collisions with walls, obstacles and other robots. Participants' research contributes to improve AGVs (Automated Guided Vehicle systems) technology. Robot@Factory is now integrated in Festival Nacional de Robotica, a yearly event which attracts lots of public, contributing also to STEM (Science, Technology, Engineering and Mathematics) popularization. MicroFactory's main contribution is different - enhancing learning and the undergraduate experience in robotics. While Robot@Factory is intended for groups with high skills, MicroFactory is supposed to attract younger and less skilled people. So, the proposed challenges were simplified. It was also designed an official robot for the MicroFactory competition. It's a 3D printed robot, based on Arduino and low cost common electronic parts. CAD files for the mechanics (and every bit of the factory scenario), the hardware schematics and most of the software can be made available to the organizers or teachers trying to implement didactic experiences involving robotics. The challenge may then be reduced from developing a robot from scratch to implementing just a small part like programming the navigation algorithm. The presented work is part of a wider Open Source project, aiming to develop project-based collaborative didactic experiences involving robotics to foster STEM education, and low-cost 3D printed educational robots based on generic electronics to support those experiences.

Abstract
This contribution presents a new system for fast and intuitive industrial robot reprogramming. It is based on a luminous marker built with high-intensity LEDs, which are captured by a set of industrial cameras. Using stereoscopy, the marker supplies 6-DoF human wrist tracking with both position and orientation data. This marker can be efficiently attached to any working tool which then provides a way to capture human skills without further intrusion in the tasks. The acquisition technique makes the tracking very robust against lighting conditions so no environment preparation is needed. The robot is automatically programmed from the demonstrated task which delivers complete abstraction of programming concepts. The system is able to perform in real time, and is low-cost starting with a single pair of industrial cameras though more can be used for improved effectiveness and accuracy. The real-time feature means that the robot is ready to perform as soon as the demonstration is over which carries no overhead of reprogramming times. Also, there is no interference with the task itself since the marker is attached to the work tool and the tracking is contactless; the human operator can then perform naturally. The test bed is a real industrial environment: a spray painting application. A prototype has been developed and installed, and is currently in operation. The tests show that the proposed system enables transferring to the machine the human ability of manipulating a spray gun.

Abstract
Purpose - Streamlining automated processes is currently undertaken by developing optimization methods and algorithms for robotic manipulators. This paper aims to present a new approach to improve streamlining of automatic processes. This new approach allows for multiple robotic manipulators commonly found in the industrial environment to handle different scenarios, thus providing a high-flexibility solution to automated processes. Design/methodology/approach - The developed system is based on a spatial discretization methodology capable of describing the surrounding environment of the robot, followed by a novel path-planning algorithm. Gazebo was the simulation engine chosen, and the robotic manipulator used was the Universal Robot 5 (UR5). The proposed system was tested using the premises of two robotic challenges: EuRoC and Amazon Picking Challenge. Findings - The developed system was able to identify and describe the influence of each joint in the Cartesian space, and it was possible to control multiple robotic manipulators safely regardless of any obstacles in a given scene. Practical implications - This new system was tested in both real and simulated environments, and data collected showed that this new system performed well in real- life scenarios, such as EuRoC and Amazon Picking Challenge. Originality/ value - The new proposed approach can be valuable in the robotics field with applications in various industrial scenarios, as it provides a flexible solution for multiple robotic manipulator path and motion planning.

Abstract
The scientific and technological development, together with the world of robotics, is constantly evolving, driven by the need to find new solutions and by the ambition of human beings to develop systems with increasingly efficiency. Consequently, it is necessary to develop planning algorithms capable of effectively and safely move a robot within a given non structured scene. Moreover, despite of the several robotic solutions available, there are still challenges to standardise a development technique able to obviate some pitfalls and limitations present in the robotic world. The Robotic Operative System (ROS) arise as the obvious solution in this regard. Throughout this project it was developed and implemented a double A* path planning methodology for automatic manipulators in the industrial environment. In this paper, it will be presented an approach with enough flexibility to be potentially applicable to different handling scenarios normally found in industrial environment.

Abstract
The robotic football competition has encouraged the participants to develop new ways of solving different problems in order to succeed in the competition. This article shows a different approach to the ball detection and recognition by the robot using a Kinect System. It has enhanced the capabilities of the depth camera in detecting and recognizing the ball during the football match. This is important because it is possible to avoid the noise that the RGB cameras are subject to for example lighting issues.

Abstract
This letter presents a novel architecture for evaluating the success of picking operations that are executed by industrial robots. It is formed by a cascade of machine learning algorithms (kNN and SVM) and uses information obtained by a 6 axis force/torque sensor and, if available, information from the built-in sensors of the robotic gripper. Beyond measuring the success or failure of the entire operation, this architecture makes it possible to detect in real-time when an object is slipping during the picking. Therefore, force and torque signatures are collected during the picking movement of the robot, which is decomposed into five different stages that allows to characterize distinct levels of success over time. Several trials were performed using an industrial robot with two different grippers for picking a long and flexible object. The experiments demonstrate the reliability of the proposed approach under different picking scenarios since, it obtained a testing performance (in terms of accuracy) up to 99.5% of successful identification of the result of the picking operations, considering an universe of 400 attempts.