This paper presents a new marker for robot programming by demonstration through motion imitation. The device is based on high intensity LEDs (light emission diodes) which are captured by a pair of industrial cameras. Using stereoscopy, the marker supplies 6-DoF (degrees of freedom) human wrist tracking with both position and orientation data. We propose a robust technique for camera and stereo calibration which maps camera coordinates directly into the desired robot frame, using a single LED. The calibration and tracking procedures are thoroughly described. The tests show that the marker presents a new robust, accurate and intuitive method for industrial robot programming. The system is able to perform in real-time and requires only a single pair of industrial cameras though more can be used for improved effectiveness and accuracy.

New and more natural human-robot interfaces are of crucial interest to the evolution of robotics. This paper addresses continuous and real-time hand gesture spotting, i.e., gesture segmentation plus gesture recognition. Gesture patterns are recognized by using artificial neural networks (ANNs) specifically adapted to the process of controlling an industrial robot. Since in continuous gesture recognition the communicative gestures appear intermittently with the non-communicative, we are proposing a new architecture with two ANNs in series to recognize both kinds of gesture. A data glove is used as interface technology. Experimental results demonstrated that the proposed solution presents high recognition rates (over 99% for a library of ten gestures and over 96% for a library of thirty gestures), low training and learning time and a good capacity to generalize from particular situations.

This paper introduces, OmniClimber II, an evolution of OmniClimber I, a novel climbing robot with high maneuverability for inspection of ferromagnetic 3D human made structures. The robot takes advantage of novelties which allows an excellent maneuverability on the structure and can adapt to flat and non-flat structures. In this paper we present the conceptual and detailed design of the robot, the implementation and improvements over the first version and a set of tests: laboratory tests on a flat surface; and a set of field tests performed on a wind turbine foundation and another field test on a curved steel structure in Mechanical Engineering, to prove the applicability of the robot on both structures.

This paper introduces a new approach to 3-D position estimation from acceleration data, i.e., a 3-D motion tracking system having a small size and low-cost magnetic and inertial measurement unit (MIMU) composed by both a digital compass and a gyroscope as interaction technology. A major challenge is to minimize the error caused by the process of double integration of accelerations due to motion (these ones have to be separated from the accelerations due to gravity). Owing to drift error, position estimation cannot be performed with adequate accuracy for periods longer than few seconds. For this reason, we propose a method to detect motion stops and only integrate accelerations in moments of effective hand motion during the demonstration process. The proposed system is validated and evaluated with experiments reporting a common daily life pick-and-place task.

This paper describes design and development of omnidirectional magnetic climbing robots with high maneuverability for inspection of ferromagnetic 3D human made structures. The main focus of this article is design, analysis and implementation of magnetic omnidirectional wheels for climbing robots. We discuss the effect of the associated problems of such wheels, e.g. vibration, on climbing robots. This paper also describes the evolution of magnetic omnidirectional wheels throughout the design and development of several solutions, resulting in lighter and smaller wheels which have less vibration and adapt better to smaller radius structures. These wheels are installed on a chassis which adapts passively to flat and curved structures, enabling the robot to climb and navigate on such structures.

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.

In this paper we describe a system designed to be used on a shop floor of a factory that produces non-flat ceramic products, where an extensive mixture of human and automatic labour is present. Today manufacturing setups rely increasingly on technology. It is common to have all sources of equipment on the shop floor, commanded by industrial PCs or PLCs connected by an industrial network to other factory resources. Also, the production systems are becoming more and more autonomous requiring less operator intervention in every day normal operation. That means using computers for controlling and supervision of the production systems, industrial networks and distributed software architectures. It means also designing application software that is really distributed in the shop floor, taking advantage of the flexibility installed by using programmable equipment.