José Mauricio S. T. Motta

A Reconfigurable System Approach to the Direct Kinematics of a 5 D.o.f Robotic Manipulator

Hardware acceleration in high performance computer systems has a particular interest for many engineering and scientific applications in which a large number of arithmetic operations and transcendental functions must be computed. In this paper a hardware architecture for computing direct kinematics of robot manipulators with 5 degrees of freedom (5 D.o.f) using floating-point arithmetic is presented for 32, 43, and 64 bit-width representations and it is implemented in Field Programmable Gate Arrays (FPGAs). The proposed architecture has been developed using several floating-point libraries for arithmetic and transcendental functions operators, allowing the designer to select (pre-synthesis) a suitable bit-width representation according to the accuracy and dynamic range, as well as the area, elapsed time and power consumption requirements of the application. Synthesis results demonstrate the effectiveness and high performance of the implemented cores on commercial FPGAs. Simulation results have been addressed in order to compute the Mean Square Error (MSE), using the Matlab as statistical estimator, validating the correct behavior of the implemented cores. Additionally, the processing time of the hardware architecture was compared with the same formulation implemented in software, using the PowerPC (FPGA embedded processor), demonstrating that the hardware architecture speeds-up by factor of 1298 the software implementation.

A prototype of a specialized robotic system for repairing hydraulic turbine blades

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.

An investigation of singularities in robot kinematic chains aiming at building robot calibration models for off-line programming

Robot Calibration is a term applied to the procedures used in determining actual values that describe the geometric dimensions and mechanical characteristics of a robot or multibody structure. A robot calibration system must consist of appropriate robot modeling techniques, accurate measurement equipment, and reliable model parameter determination methods. For practical improvement of a robot’s absolute accuracy, error compensation methods are required that use calibration results. Important to robot calibration methods is an accurate kinematic model that has identifiable parameters. This parameterized kinematic model must be complete, continuous and minimal. This work concerns to the implementation of techniques to optimize kinematic models for robot calibration through numerical optimization of the mathematical model. The optimized model is then used to compensate the model errors in an off-line programming system, odel accuracy. The optimized model can be n, through automatic assignment of joint coordinate systems and geometric parameter to the robot links. Assignment of coordinate systems by this technique avoids model singularities that usually spoil robot calibration results.

Experimental validation of a 3-D vision-based measurement system applied to robot calibration

One of the problems that slows the development of off-line programming is the low static and dynamic positioning accuracy of robots. Robot calibration improves the positioning accuracy and can also be used as a diagnostic tool in robot production and maintenance. A large number of robot measurement systems are now available commercially. Yet, there is a dearth of systems that are portable, accurate and low cost. In this work a measurement system that can fill this gap in local calibration is presented. The measurement system consists of a single CCD camera mounted on the robot tool flange with a wide angle lens, and uses space resection models to measure the end-effector pose relative to a world coordinate system, considering radial distortions. Scale factors and image center are obtained with innovative techniques, making use of a multiview approach. The target plate consists of a grid of white dots impressed on a black photographic paper, and mounted on the sides of a 90-degree angle plate. Results show that the achieved average accuracy varies from 0.2mm to 0.4mm, at distances from the target from 600mm to 1000mm respectively, with different camera orientations.

An FPGA-based omnidirectional vision sensor for motion detection on mobile robots

This work presents the development of an integrated hardware/software sensor system for moving object detection and distance calculation, based on background subtraction algorithm. The sensor comprises a catadioptric system composed by a camera and a convex mirror that reflects the environment to the camera from all directions, obtaining a panoramic view. The sensor is used as an omnidirectional vision system, allowing for localization and navigation tasks of mobile robots. Several image processing operations such as filtering, segmentation and morphology have been included in the processing architecture. For achieving distance measurement, an algorithm to determine the center of mass of a detected object was implemented. The overall architecture has been mapped onto a commercial low-cost FPGA device, using a hardware/software co-design approach, which comprises a Nios II embedded microprocessor and specific image processing blocks, which have been implemented in hardware. The background subtraction algorithm was also used to calibrate the system, allowing for accurate results. Synthesis results show that the system can achieve a throughput of 26.6 processed frames per second and the performance analysis pointed out that the overall architecture achieves a speedup factor of 13.78 in comparison with a PC-based solution running on the real-time operating system xPC Target.

Real-Time Measurement of Width and Height of Weld Beads in GMAW Processes

Associated to the weld quality, the weld bead geometry is one of the most important parameters in welding processes. It is a significant requirement in a welding project, especially in automatic welding systems where a specific width, height, or penetration of weld bead is needed. This paper presents a novel technique for real-time measuring of the width and height of weld beads in gas metal arc welding (GMAW) using a single high-speed camera and a long-pass optical filter in a passive vision system. The measuring method is based on digital image processing techniques and the image calibration process is based on projective transformations. The measurement process takes less than 3 milliseconds per image, which allows a transfer rate of more than 300 frames per second. The proposed methodology can be used in any metal transfer mode of a gas metal arc welding process and does not have occlusion problems. The responses of the measurement system, presented here, are in a good agreement with off-line data collected by a common laser-based 3D scanner. Each measurement is compare using a statistical Welch’s t-test of the null hypothesis, which, in any case, does not exceed the threshold of significance level α = 0.01, validating the results and the performance of the proposed vision system.

Modeling, optimizing and simulating robot calibration with accuracy improvement

This work describes techniques for modeling, optimizing and simulating calibration processes of robots using off-line programming. The identification of geometric parameters of the nominal kinematic model is optimized using techniques of numerical optimization of the mathematical model. The simulation of the actual robot and the measurement system is achieved by introducing random errors representing their physical behavior and its statistical repeatability. An evaluation of the corrected nominal kinematic model brings about a clear perception of the influence of distinct variables involved in the process for a suitable planning, and indicates a considerable accuracy improvement when the optimized model is compared to the non-optimized one.

Inverse Kinematics and Model Calibration Optimization of a Five-D.O.F. Robot for Repairing the Surface Profiles of Hydraulic Turbine Blades

This paper presents and discusses the results of an ongoing R&D project aiming to design and build a fully automated prototype of a specialized spherical robotic welding system for repairing hydraulic turbine surfaces eroded by cavitation pitting and/or cracks produced by cyclic loading. The system has an embedded vision sensor built to acquire range images by laser scanning over the blades surface and produce 3D models to locate the damaged spots to be registered in a 3D coordinate system into the robot controller, enabling the robot to repair the flaws automatically by welding in layers. The paper is focused on the robot kinematic model and describes an iterative algorithm to process the inverse kinematics with only five degrees-of-freedom. The algorithm makes use of data collected from a vision sensor to ensure that the welding gun axis is perpendicular to the blades surface. Besides this, it proposes a modelling and optimization mathematical routine for more efficient robot calibration, which can be used with any type of robot. This robot calibration optimization scheme finds the optimal error parameter vector based on the condition number of the manipulator transformation composed from the partial derivatives of the error parameters. Experimental results proved both the iterative algorithm to perform the inverse kinematics and the technique to optimize robot calibration to be very efficient.

FPGA implementation of the EKF algorithm for localization in mobile robotics using a unified hardware module approach

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.

Hardware Architecture of the EKF Prediction Stage applied to mobile robot localization

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.