Abstract
In this paper, an adaptive and low-cost robotic coating platform for small production series is presented. This new platform presents a flexible architecture that enables fast/automatic system adaptive behaviour without human intervention. The concept is based on contactless technology, using artificial vision and laser scanning to identify and characterize different workpieces travelling on a conveyor. Using laser triangulation, the workpieces are virtually reconstructed through a simplified cloud of three-dimensional (3D) points. From those reconstructed models, several algorithms are implemented to extract information about workpieces profile (pattern recognition), size, boundary and pose. Such information is then used to on-line adjust the "base" robot programmes. These robot programmes are off-line generated from a 3D computer-aided design model of each different workpiece profile. Finally, the robotic manipulator executes the coating process after its "base" programmes have been adjusted. This is a low-cost and fully autonomous system that allows adapting the robot's behaviour to different manufacturing situations. It means that the robot is ready to work over any piece at any time, and thus, small production series can be reduced to as much as a one-object series. No skilled workers and large setup times are needed to operate it. Experimental results showed that this solution proved to be efficient and can be applied not only for spray coating purposes but also for many other industrial processes (automatic manipulation, pick-and-place, inspection, etc.).

Abstract

Abstract
Most of industrial robots are still programmed using the typical teaching process, through the use of the robot teach pendant. In this paper is proposed an accelerometer-based system to control an industrial robot using two low-cost and small 3-axis wireless accelerometers. These accelerometers are attached to the human arms, capturing its behavior (gestures and postures). An Artificial Neural Network (ANN) trained with a back-propagation algorithm was used to recognize arm gestures and postures, which then will be used as input in the control of the robot. The aim is that the robot starts the movement almost at the same time as the user starts to perform a gesture or posture (low response time). The results show that the system allows the control of an industrial robot in an intuitive way. However, the achieved recognition rate of gestures and postures (92%) should be improved in future, keeping the compromise with the system response time (160 milliseconds). Finally, the results of some tests performed with an industrial robot are presented and discussed.

Abstract
Purpose - The purpose of this paper is to present a CAD-based human-robot interface that allows non-expert users to teach a robot in a manner similar to that used by human beings to teach each other. Design/methodology/approach - Intuitive robot programming is achieved by using CAD drawings to generate robot programs off-line. Sensory feedback allows minimization of the effects of uncertainty, providing information to adjust the robot paths during robot operation. Findings - It was found that it is possible to generate a robot program from a common CAD drawing and run it without any major concerns about calibration or CAD model accuracy. Research limitations/implications - A limitation of the proposed system has to do with the fact that it was designed to be used for particular technological applications. Practical implications - Since most manufacturing companies have CAD packages in their facilities today, CAD-based robot programming may be a good option to program robots without the need for skilled robot programmers. Originality/value - The paper proposes a new CAD-based robot programming system. Robot programs are directly generated from a CAD drawing "running" on a commonly available 3D CAD package (Autodesk Inventor) and not from a commercial, computer aided robotics (CAR) software, making it a simple CAD integrated solution. This is a low-cost and low-setup time system where no advanced robot programming skills are required to operate it. In summary, robot programs are generated with a high-level of abstraction from the robot language.

Abstract
Purpose - Most industrial robots are still programmed using the typical teaching process, through the use of the robot teach pendant. This is a tedious and time-consuming task that requires some technical expertise, and hence new approaches to robot programming are required. The purpose of this paper is to present a robotic system that allows users to instruct and program a robot with a high-level of abstraction from the robot language. Design/methodology/approach - The paper presents in detail a robotic system that allows users, especially non-expert programmers, to instruct and program a robot just showing it what it should do, in an intuitive way. This is done using the two most natural human interfaces (gestures and speech), a force control system and several code generation techniques. Special attention will be given to the recognition of gestures, where the data extracted from a motion sensor (three-axis accelerometer) embedded in the Wii remote controller was used to capture human hand behaviours. Gestures (dynamic hand positions) as well as manual postures (static hand positions) are recognized using a statistical approach and artificial neural networks. Findings - It is shown that the robotic system presented is suitable to enable users without programming expertise to rapidly create robot programs. The experimental tests showed that the developed system can be customized for different users and robotic platforms. Research limitations/implications - The proposed system is tested on two different robotic platforms. Since the options adopted are mainly based on standards, it can be implemented with other robot controllers without significant changes. Future work will focus on improving the recognition rate of gestures and continuous gesture recognition. Practical implications The key contribution of this paper is that it offers a practical method to program robots by means of gestures and speech, improving work efficiency and saving time. Originality/value - This paper presents an alternative to the typical robot teaching process, extending the concept of human-robot interaction and co-worker scenario. Since most companies do not have engineering resources to make changes or add new functionalities to their robotic manufacturing systems, this system constitutes a major advantage for small- to medium-sized enterprises.

Abstract
The inspiration for this paper comes from a successful experiment conducted with students in the "Mobile Robots" course in the fifth year of the integrated Master's program in the Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto (FEUP), Porto, Portugal. One of the topics in this Mobile Robots course is "Localization of Mobile Robots using the Extended Kalman Filter in a LEGO NXT," which gives the students the opportunity to study the concepts of localization. This experiment comes within the framework of teaching localization concepts in mobile robotics and focuses primarily on explaining the Kalman filter concept. It involves a specific tool developed by the authors and based on LEGO NXT technology. The work presented here could be a helpful guide for teaching concepts related to localization in mobile robotics to ensure adequate understanding of the concept and of the use of the extended Kalman filter (EKF). The LegoFeup robot described here was built using a LEGO Mindstorms NXT and tested both in simulation and in real scenarios. Based on the results obtained, the authors concluded that the developed tool is effective in motivating students. The implementation of the tool, the structure of the Mobile Robots course, and the criteria for student assessment are described in this paper.