Fused Smart Sensor Network for Multi-Axis Forward Kinematics Estimation in Industrial Robots

Flexible manipulator robots have a wide industrial application. Robot performance requires sensing its position and orientation adequately, known as forward kinematics. Commercially available, motion controllers use high-resolution optical encoders to sense the position of each joint which cannot detect some mechanical deformations that decrease the accuracy of the robot position and orientation. To overcome those problems, several sensor fusion methods have been proposed but at expenses of high-computational load, which avoids the online measurement of the joint’s angular position and the online forward kinematics estimation. The contribution of this work is to propose a fused smart sensor network to estimate the forward kinematics of an industrial robot. The developed smart processor uses Kalman filters to filter and to fuse the information of the sensor network. Two primary sensors are used: an optical encoder, and a 3-axis accelerometer. In order to obtain the position and orientation of each joint online a field-programmable gate array (FPGA) is used in the hardware implementation taking advantage of the parallel computation capabilities and reconfigurability of this device. With the aim of evaluating the smart sensor network performance, three real-operation-oriented paths are executed and monitored in a 6-degree of freedom robot

Virtual Sensor for Kinematic Estimation of Flexible Links in Parallel Robots

The control of flexible link parallel manipulators is still an open area of research, endpoint trajectory tracking being one of the main challenges in this type of robot. The flexibility and deformations of the limbs make the estimation of the Tool Centre Point (TCP) position a challenging one. Authors have proposed different approaches to estimate this deformation and deduce the location of the TCP. However, most of these approaches require expensive measurement systems or the use of high computational cost integration methods. This work presents a novel approach based on a virtual sensor which can not only precisely estimate the deformation of the flexible links in control applications (less than 2% error), but also its derivatives (less than 6% error in velocity and 13% error in acceleration) according to simulation results. The validity of the proposed Virtual Sensor is tested in a Delta Robot, where the position of the TCP is estimated based on the Virtual Sensor measurements with less than a 0.03% of error in comparison with the flexible approach developed in ADAMS Multibody Software.This work was supported in part by the Spanish Ministry of Economy and Competitiveness under grant BES-2013-066142, UPV/EHU's PPG17/56 projects, Spanish Ministry of Economy and Competitiveness' MINECO & FEDER inside DPI-2012-32882 project and the Basque Country Government's (GV/EJ) under PRE-2014-1-152 and BFI-2012-223 grants and under recognized research group IT914-16

Design and Implementation of Piecewise-Affine Observers for Nonlinear Systems

This thesis is divided into two main parts. The contribution of the first part is to design a continuous-time Piecewise-Affine (PWA) observer for a class of nonlinear systems. It is shown that the state estimation error is ultimately bounded. The bound on the state estimation error depends on the PWA approximation error. Moreover, it is shown that the state estimation error is still convergent and ultimately bounded when the output of the system is only available at sampling instants. The proof of convergence is presented in two parts: conditions dependent on the sampling time and conditions independent of the sampling time. In addition, ultimate boundedness of the state estimation error is proven in the presence of norm bounded measurement noise. It is shown that the bound on the state estimation error is dependent on the sampling time, PWA approximation error and the bound on the norm of the noise. The proposed approach for observer design leads to a convex optimization which can be solved efficiently using available software packages. The contribution of the second part is to implement the proposed PWA observer on a real setup of a wheeled mobile robot (WMR) available at the Hybrid Control Systems (HYCONS) Laboratory of Concordia University. Although some researchers have applied different types of observers to experimental applications, practical implementation of PWA observers has not been given much attention by researchers. In this thesis for the first time a PWA observer is applied to the WMR. The WMR is an example of a nonlinear system with a sampled output in the presence of measurement noise. The results of the experimental implementation validate the proposed theoretical results in the first part

Experimental Comparison of Observers for Tool Position Estimation of Industrial Robots

This paper investigates methods for tool position estimation of industrial robots. It is assumed that the motor angular position and the tool acceleration are measured. The considered observers are different versions of the extended Kalman filter as well as a deterministic observer. A method for tuning the observers is suggested and the robustness of the methods is investigated. The observers are evaluated experimentally on a commercial industrial robot