Abstract
In this paper an analysis of MicroFactory is carried out and its potential for generating a diversified set of didactic experiences is evaluated. MicroFactory is a robotic competition based on a previously existing competition called Robot@Factory. 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 two warehouses, and eight processing machines. The flow of the materials inside the factory starts at the Incoming Warehouse and ends at the Outgoing Warehouse, eventually passing through one or more processing machines. The robots must collect, transport and position the materials along the process, having to self-localize and navigate while avoiding collisions with walls, obstacles and other robots. There is the option of following predefined tracks present on the floor to ease the navigation problem. Robot@Factory poses challenges like dynamic task scheduling, robot cooperation, trajectory planning, robot navigation with obstacle avoidance, robot self-localization and materials identification and manipulation. Related research contributes to improve AGVs (Automated Guided Vehicle systems) technology. Presently this competition is 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 was conceived to be low-cost and easily implementable in a small space, be it a classroom or the school robotics club. The ground area of the factory scenario was reduced to approximately one ninth of its original value. The scenario materials were simplified -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 and now they are passive. Besides the competition scenario it was also conceived a prototype robot for the competition. It's a 3D printed robot, based on an Arduino board and accessible electronic parts. The creation of this competition is part of a wider Open Source project, aiming to develop project-based collaborative didactic experiences involving robotics and low-cost 3D printed educational robots based on generic electronics to support those experiences. Currently efforts are being dedicated to the inclusion of more sensors in the competition robot, namely low-cost distance sensors and a weight sensor at the claws, the inclusion of different kinds of motors, the development of a new version of the robot incorporating a Raspberry Pi board, the development of a very precise robot localization system, and the conception of a diversified set of didactic experiences based on the MicroFactory competition. This article presents an analysis of MicroFactory and of its inherent challenges. Through this analysis it will be possible to identify topics that can be taught and learned while developing robots to participate in the competition, and to collect elements that will be very useful in the planning and implementation of didactic experiences that work those topics.

Abstract
In this paper it is discussed the proposal of a small robot prototype to be applied in the MicroFactory competition, a downsized version of the Robot@Factory competition. The MicroFactory is intended to help junior competitors to make the transition from the Junior Leagues to the senior competition Robot@Factory. The Robot@Factory competition takes place in an emulated factory plant, where Automatic Guided Vehicles (AGVs) must cooperate to perform tasks. To accomplish their goals the AGVS must deal with localization, navigation, scheduling and cooperation problems, that must be solved autonomously.

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.