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In this paper it is proposed a web-based compliant solution for teleoperation of an industrial robot as a contribution to the increasing technological requirements in industry, using open source programming languages and standard communication protocols to provide a complete and flexible solution for academics and industrial applications. With the recent developments of Industry 4.0 concepts, as Cyber-Physical Systems (CPS), Internet of Things (IoT) and Cloud, there is a need for non-proprietary, robust and efficient architectures compatible with any industrial robotic supplier system. Proposed solution provides an open architecture for an emerging generation of Cloud robotics services for advanced manufacturing challenges, using virtual and real environments for architecture validation with the support of ever-present Socket communication method.

In this paper a Hardware Architecture for computing the Extended Kalman Filter (EKF) is presented which is addressed to solve the self-localization problem of autonomous mobile robots. In this case, the overall EKF algorithm has been implemented in hardware over an Altera Cyclone IV FPGA with a Nios II processor, in which the latter is used only for interfacing and communication tasks. The achieved implementation has been adapted and applied to the mobile platform Pioneer 3AT (P3AT) for validation task. The prediction stage of the EKF algorithm was based on a dead-reckoning system model and the estimation stage on a measurement system which uses a sensor of Laser Range Finder (LRF). The proposed architecture has been designed considering a Unified Hardware Module approach using floating-point arithmetic operators, allowing the operations to be computed with large precision and dynamic range. Furthermore, several metrics have been used to evaluate the system performance, measuring both FPGA resources consumption, power consumption and execution time. Finally, the suitability of reconfigurable devices for such kind of applications has been verified and also discussed.

This work presents an FPGA-based Hardware Architecture to implement the Prediction Stage of the Extended Kalman Filter (EKF) applied to the localization problem in mobile robotics. The algorithm has been implemented and run on an Altera Cyclone IV FPGA with a Nios II processor, being adapted and applied to the mobile platform Pioneer 3AT (P3AT). The prediction stage was based on a dead-reckoning system model and its architecture was designed for floating-point representation. In this project the complete EKF was also implemented considering an estimation stage hardware architecture previously developed using a Laser Range Finder (LRF) sensor, producing an overall balanced implementation. Finally, it was evaluated the system performance and suitability, measuring FPGA resources consumption and comparing execution time with a software solution.

This article presents the steps of an ongoing R&D project aiming at designing and constructing a prototype of a specialized welding robotic system for repairing the surface of hydraulic turbine blades eroded by cavitation pitting and/or cracked by cyclic loading, reducing human risks and increasing the efficiency of the process. The robotic system has a spherical topology with 5 d.o.f., electric stepper and DC servo motors. The system has an embedded vision sensor built to produce range images by scanning laser beams on the blade surface and to construct 3-D models to locate the damaged spots to be recorded into the robot controller in 3-D coordinates, enabling the robot to repair the flaws automatically by welding in layers. The robot prototype is at final stages to be fully operational, with some remaining procedures such as computer systems integration and calibration for final tests and commissioning.

This article presents an ongoing R&D project aiming at designing and constructing a specialized welding robotic system for repairing hydraulic turbine blades eroded by cavitation pitting, reducing human risks and increasing the efficiency of the process. The robotic system has a spherical topology with 5 degrees of freedom, electric stepper motors and a 2.5m-diameter workspace. The system has an embedded measurement system with a vision sensor built to produce range images by scanning laser beams on the blade surface. The range images are used to construct 3-D models of the blade surface and locate the damaged spots to be recorded into the robot controller in 3-D coordinates, enabling the robot to repair the flaws automatically by welding in layers. The robot controller and measurement system are built in an FPGA based reconfigurable system. The welding process is the GMAW with a tubular metal cored electrode with a pulsed GMA welding machine.

The sequential behavior of general purpose processors presents limitations in applications that require high processing speeds. One of the advantages of FPGAs implementations is the parallel process capability, allowing acceleration of complex algorithms. Nowadays it is common to find FPGA implementations in applications requiring high speed processing. In this paper a hardware architecture for computing direct kinematics of robot manipulators using floating-point arithmetic is presented for 32, 43 and 64 bit-width representations. Otherwise, the processing time of the hardware architecture is compared with the same formulation implemented in software, using the PowerPC (FPGA embedded processor). The proposed architecture was validated using Matlab results as a statistical estimator in order to compute the Mean Square Error (MSE). Synthesis and simulation results demonstrate the accuracy and high performance of the implemented hardware architecture.