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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper was to propose a method based on an Artificial Neural Network and a real-time vision algorithm, to learn welding skills in industrial robotics. Design/methodology/approach -By using an optic camera to measure the bead geometry (width and height), the authors propose a real-time computer vision algorithm to extract training patterns and to enable an industrial robot to acquire and learn autonomously the welding skill. To test the approach, an industrial KUKA robot and a welding gas metal arc welding machine were used in a manufacturing cell. Findings -Several data analyses are described, showing empirically that industrial robots can acquire the skill even if the specific welding parameters are unknown. Research limitations/implications -The approach considers only stringer beads. Weave bead and bead penetration are not considered. Practical implications -With the proposed approach, it is possible to learn specific welding parameters despite of the material, type of robot or welding machine. This is due to the fact that the feedback system produces automatic measurements that are labelled prior to the learning process. Originality/value -The main contribution is that the complex learning process is reduced into an input-process-output system, where the process part is learnt automatically without human supervision, by registering the patterns with an automatically calibrated vision system.

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Earth observation with unmanned aerial vehicles (UAVs) offers an extraordinary opportunity to bridge the gap between field observations and traditional air and space-borne remote sensing. In this regard, ground landing stations (GLS) systems play a central role to increase the time and area coverage of UAV missions. Bluetooth low energy (BLE) technology and the received signal strength indicator (RSSI) techniques have been proposed for target location during UAV landing. However, these RSSI-based techniques present a lack of precision due to the propagation medium characteristics, which leads to UAV position vagueness. In this sense, the development of a novel low-cost GLS system for UAV tracking and landing is proposed. The GLS system has been embodied for the purpose of testing the UAV landing navigation capability. The maximum likelihood estimator (MLE) algorithm is addressed on an embedded microcontroller for the position estimation based on the RSSI acquired from an array of BLE devices. Experimental results demonstrate the feasibility and accuracy of the ground landing station system, achieving average errors of less than 0.04 m with the UAV-MLE target position estimation approach. This 0.04 m distance represents an order of magnitude increase in location precision over other currently available solutions. In many cases, this increased precision can enable more innovative docking mechanisms, less likelihood of mishaps in docking, and also quicker docking. It may also facilitate docking procedures where the docking station is itself moving, which may be the case if the docking unit is a mobile ground rover.

For control applications, the angular velocity of the drive crank of a four-bar mechanism is traditionally assumed to be constant. In this paper, we propose control of variable velocity of the drive crank to obtain the desired output motions for the coupler point. To estimate the reference trajectory for the crank velocity, a neural network is trained with data from the kinematic model. The control law is designed from feedback linearization of the tracking error dynamics and a Proportional–Integral–Derivative (PID) controller. The applicability of the proposed scheme is validated through simulations for three variable speed profiles, obtaining excellent results from the system.