Efficient image mosaicing for multi-robot visual underwater mapping

Robotic platforms have advanced greatly in terms of their remote sensing capabilities, including obtaining optical information using cameras. Alongside these advances, visual mapping has become a very active research area, which facilitates the mapping of areas inaccessible to humans. This requires the efficient processing of data to increase the final mosaic quality and computational efficiency. In this paper, we propose an efficient image mosaicing algorithm for large area visual mapping in underwater environments using multiple underwater robots. Our method identifies overlapping image pairs in the trajectories carried out by the different robots during the topology estimation process, being this a cornerstone for efficiently mapping large areas of the seafloor. We present comparative results based on challenging real underwater datasets, which simulated multi-robot mapping

Ambient intelligence application based on environmental measurements performed with an assistant mobile robot

This paper proposes the use of an autonomous assistant mobile robot in order to monitor the environmental conditions of a large indoor area and develop an ambient intelligence application. The mobile robot uses single high performance embedded sensors in order to collect and geo-reference environmental information such as ambient temperature, air velocity and orientation and gas concentration. The data collected with the assistant mobile robot is analyzed in order to detect unusual measurements or discrepancies and develop focused corrective ambient actions. This paper shows an example of the measurements performed in a research facility which have enabled the detection and location of an uncomfortable temperature profile inside an office of the research facility. The ambient intelligent application has been developed by performing some localized ambient measurements that have been analyzed in order to propose some ambient actuations to correct the uncomfortable temperature profile.

Sensor Fusion of Proprioception, Force and Vision in Estimation and Robot

Sensor-based robot control allows manipulation in dynamic environments with uncertainties. Vision is a versatile low-cost sensory modality, but low sample rate, high sensor delay and uncertain measurements limit its usability, especially in strongly dynamic environments. Force is a complementary sensory modality allowing accurate measurements of local object shape when a tooltip is in contact with the object. In multimodal sensor fusion, several sensors measuring different modalities are combined to give a more accurate estimate of the environment. As force and vision are fundamentally different sensory modalities not sharing a common representation, combining the information from these sensors is not straightforward. In this thesis, methods for fusing proprioception, force and vision together are proposed. Making assumptions of object shape and modeling the uncertainties of the sensors, the measurements can be fused together in an extended Kalman filter. The fusion of force and visual measurements makes it possible to estimate the pose of a moving target with an end-effector mounted moving camera at high rate and accuracy. The proposed approach takes the latency of the vision system into account explicitly, to provide high sample rate estimates. The estimates also allow a smooth transition from vision-based motion control to force control. The velocity of the end-effector can be controlled by estimating the distance to the target by vision and determining the velocity profile giving rapid approach and minimal force overshoot. Experiments with a 5-degree-of-freedom parallel hydraulic manipulator and a 6-degree-of-freedom serial manipulator show that integration of several sensor modalities can increase the accuracy of the measurements significantly.

Use of neural networks in robot positioning of large flexible redundant manipulators

Deflection compensation of flexible boom structures in robot positioning is usually done using tables containing the magnitude of the deflection with inverse kinematics solutions of a rigid structure. The number of table values increases greatly if the working area of the boom is large and the required positioning accuracy is high. The inverse kinematics problems are very nonlinear, and if the structure is redundant, in some cases it cannot be solved in a closed form. If the structural flexibility of the manipulator arms is taken into account, the problem is almost impossible to solve using analytical methods. Neural networks offer a possibility to approximate any linear or nonlinear function. This study presents four different methods of using neural networks in the static deflection compensation and inverse kinematics solution of a flexible hydraulically driven manipulator. The training information required for training neural networks is obtained by employing a simulation model that includes elasticity characteristics. The functionality of the presented methods is tested based on the simulated and measured results of positioning accuracy. The simulated positioning accuracy is tested in 25 separate coordinate points. For each point, the positioning is tested with five different mass loads. The mean positioning error of a manipulator decreased from 31.9 mm to 4.1 mm in the test points. This accuracy enables the use of flexible manipulators in the positioning of larger objects. The measured positioning accuracy is tested in 9 separate points using three different mass loads. The mean positioning error decreased from 10.6 mm to 4.7 mm and the maximum error from 27.5 mm to 11.0 mm.

Novel Robot Solutions for Carrying out Field Joint Welding and Machining in the Assembly of the Vacuum Vessel of ITER

It is necessary to use highly specialized robots in ITER (International Thermonuclear Experimental Reactor) both in the manufacturing and maintenance of the reactor due to a demanding environment. The sectors of the ITER vacuum vessel (VV) require more stringent tolerances than normally expected for the size of the structure involved. VV consists of nine sectors that are to be welded together. The vacuum vessel has a toroidal chamber structure. The task of the designed robot is to carry the welding apparatus along a path with a stringent tolerance during the assembly operation. In addition to the initial vacuum vessel assembly, after a limited running period, sectors need to be replaced for repair. Mechanisms with closed-loop kinematic chains are used in the design of robots in this work. One version is a purely parallel manipulator and another is a hybrid manipulator where the parallel and serial structures are combined. Traditional industrial robots that generally have the links actuated in series are inherently not very rigid and have poor dynamic performance in high speed and high dynamic loading conditions. Compared with open chain manipulators, parallel manipulators have high stiffness, high accuracy and a high force/torque capacity in a reduced workspace. Parallel manipulators have a mechanical architecture where all of the links are connected to the base and to the end-effector of the robot. The purpose of this thesis is to develop special parallel robots for the assembly, machining and repairing of the VV of the ITER. The process of the assembly and machining of the vacuum vessel needs a special robot. By studying the structure of the vacuum vessel, two novel parallel robots were designed and built; they have six and ten degrees of freedom driven by hydraulic cylinders and electrical servo motors. Kinematic models for the proposed robots were defined and two prototypes built. Experiments for machine cutting and laser welding with the 6-DOF robot were carried out. It was demonstrated that the parallel robots are capable of holding all necessary machining tools and welding end-effectors in all positions accurately and stably inside the vacuum vessel sector. The kinematic models appeared to be complex especially in the case of the 10-DOF robot because of its redundant structure. Multibody dynamics simulations were carried out, ensuring sufficient stiffness during the robot motion. The entire design and testing processes of the robots appeared to be complex tasks due to the high specialization of the manufacturing technology needed in the ITER reactor, while the results demonstrate the applicability of the proposed solutions quite well. The results offer not only devices but also a methodology for the assembly and repair of ITER by means of parallel robots.

Internal standard versus external standard calibration: an uncertainty case study of a liquid chromatography analysis

Traditionally, in the cigarettes industry, the determination of ammonium ion in the mainstream smoke is performed by ion chromatography. This work studies this determination and compares the results of this technique with the use of external and internal standard calibration. A reference cigarette sample presented measurement uncertainty of 2.0 μg/cigarette and 1.5 μg/cigarette, with external and internal standard, respectively. It is observed that the greatest source of uncertainty is the bias correction factor and that it is even more significant when using external standard, confirming thus the importance of internal standardization for this correction.

Simultaneous spectrophotometric determination of lamivudine and zidovudine in fixed dose combinations using multivariate calibration

The simultaneous determination of two or more active components in pharmaceutical preparations, without previous chemical separation, is a common analytical problem. Published works describe the determination of AZT and 3TC separately, as raw material or in different pharmaceutical preparations. In this work, a method using UV spectroscopy and multivariate calibration is described for the simultaneous measurement of 3TC and AZT in fixed dose combinations. The methodology was validated and applied to determine the AZT+3TC contents in tablets from five different manufacturers, as well as their dissolution profile. The results obtained employing the proposed methodology was similar to methods using first derivative technique and HPLC.

UV determination of epinephrine, uric acid, and acetaminophen in pharmaceutical formulations and some human body fluids using multivariate calibration

In this work, a spectrophotometric methodology was applied in order to determine epinephrine (EP), uric acid (UA), and acetaminophen (AC) in pharmaceutical formulations and spiked human serum, plasma, and urine by using a multivariate approach. Multivariate calibration methods such as partial least squares (PLS) methods and its derivates were used to obtain a model for simultaneous determination of EP, UA and AC with good figures of merit and mixture design was in the range of 1.8 - 35.3, 1.7 - 16.8, and 1.5 - 12.1 µg mL-1. The 2nd derivate PLS showed recoveries of 95.3 - 103.3, 93.3 - 104.0, and 94.0 - 105.5 µg mL-1 for EP, UA, and AC, respectively.

Learning Robot Motions from Visual Sensing

Learning from demonstration becomes increasingly popular as an efficient way of robot programming. Not only a scientific interest acts as an inspiration in this case but also the possibility of producing the machines that would find application in different areas of life: robots helping with daily routine at home, high performance automata in industries or friendly toys for children. One way to teach a robot to fulfill complex tasks is to start with simple training exercises, combining them to form more difficult behavior. The objective of the Master’s thesis work was to study robot programming with visual input. Dynamic movement primitives (DMPs) were chosen as a tool for motion learning and generation. Assuming a movement to be a spring system influenced by an external force, making this system move, DMPs represent the motion as a set of non-linear differential equations. During the experiments the properties of DMP, such as temporal and spacial invariance, were examined. The effect of the DMP parameters, including spring coefficient, damping factor, temporal scaling, on the trajectory generated were studied.

Mallinsovitusta käyttävä konenäköohjelmisto teollisuusrobotin ohjaukseen

Tässä työssä kehitettiin teollisuusrobottijärjestelmiin soveltuva, mallinsovitusta hyödyntävä konenäköohjelmisto. Yleiskäyttöiseksi tarkoitettuun ohjelmistoon tehtiin toiminnot konenäköjärjestelmän kalibrointiin, mallinsovitukseen käytettävien mallien hallintaan ja tulosten välitykseen teollisuusroboteille. Ohjelmiston tuli olla myös niin helppokäyttöinen, että sen käyttö onnistuu lyhyellä koulutuksella. Ohjelmistoa sovellettiin puuikkunapuitteiden robotisoituun maalausjärjestelmään. Maalausjärjestelmästä onnistuttiin tekemään automaattinen, tuotteisiin mukautuva ja virhetilanteista toipuva pitkälti toimitetun konenäköjärjestelmän ansiosta.

Mobile Teleoperation of a Mobile Robot

This thesis describes the design and implementation of a graphical application on a mobile device to teleoperate a mobile robot. The department of information technology in Lappeenranta University conducts research in robotics, and the main motivation was to extend the available teleoperation applications for mobile devices. The challenge was to port existing robot software library onto an embedded device platform, then develop a suitable user interface application that provides sufficient functionality to perform teleoperation tasks over a wireless communication network. This thesis involved investigating previous teleoperation applications and conducted similar experiments to test and evaluate the designed application for functional activity and measure performance on a mobile device which have been identified and achieved. The implemented solution offered good results for navigation purposes particularly for teleoperating a visible robot and suggests solutions for exploration when no environment map for the operator is present.

Flow-batch analyser for preparation of calibration standard mixtures in simultaneous multicomponent spectrometric analysis

The application of multivariate calibration techniques to multicomponent analysis by UV-VIS molecular absorption spectrometry is a powerful tool for simultaneous determination of several chemical species. However, when this methodology is accomplished manually, it is slow and laborious, consumes high amounts of reagents and samples, is susceptible to contaminations and presents a high operational cost. To overcome these drawbacks, a flow-batch analyser is proposed in this work. This analyser was developed for automatic preparation of standard calibration and test (or validation) mixtures. It was applied to the simultaneous determination of Cu2+, Mn2+ and Zn2+ in polyvitaminic and polymineral pharmaceutical formulations, using 4-(2-piridilazo) resorcinol as reagent and a UV-VIS spectrophotometer with a photodiode array detector. The results obtained with the proposed system are in good agreement with those obtained by flame atomic absorption spectrometry, which was employed as reference method. With the proposed analyser, the preparation of calibration and test mixtures can be accomplished about four hours, while the manual procedure requires at least two days. Moreover, it consumes smaller amounts of reagents and samples than the manual procedure. After the preparation of calibration and test mixtures, 60 samples h-1 can be carried out with the proposed flow-batch analyser.

Determination of sulfur in diesel using ATR/FTIR and multivariate calibration

The aim of this present work was to provide a more fast, simple and less expensive to analyze sulfur content in diesel samples than by the standard methods currently used. Thus, samples of diesel fuel with sulfur concentrations varying from 400 and 2500 mgkg-1 were analyzed by two methodologies: X-ray fluorescence, according to ASTM D4294 and by Fourier transform infrared spectrometry (FTIR). The spectral data obtained from FTIR were used to build multivariate calibration models by partial least squares (PLS). Four models were built in three different ways: 1) a model using the full spectra (665 to 4000 cm-1), 2) two models using some specific spectrum regions and 3) a model with variable selected by classic method of variable selection stepwise. The model obtained by variable selection stepwise and the model built with region spectra between 665 and 856 cm-1 and 1145 and 2717 cm-1 showed better results in the determination of sulfur content.

Learning Robot Environment through Simulation

Traditionally simulators have been used extensively in robotics to develop robotic systems without the need to build expensive hardware. However, simulators can be also be used as a “memory”for a robot. This allows the robot to try out actions in simulation before executing them for real. The key obstacle to this approach is an uncertainty of knowledge about the environment. The goal of the Master’s Thesis work was to develop a method, which allows updating the simulation model based on actual measurements to achieve a success of the planned task. OpenRAVE was chosen as an experimental simulation environment on planning,trial and update stages. Steepest Descent algorithm in conjunction with Golden Section search procedure form the principle part of optimization process. During experiments, the properties of the proposed method, such as sensitivity to different parameters, including gradient and error function, were examined. The limitations of the approach were established, based on analyzing the regions of convergence.

Design and development of the architecture of an agricultural mobile robot

Parameters such as tolerance, scale and agility utilized in data sampling for using in Precision Agriculture required an expressive number of researches and development of techniques and instruments for automation. It is highlighted the employment of methodologies in remote sensing used in coupled to a Geographic Information System (GIS), adapted or developed for agricultural use. Aiming this, the application of Agricultural Mobile Robots is a strong tendency, mainly in the European Union, the USA and Japan. In Brazil, researches are necessary for the development of robotics platforms, serving as a basis for semi-autonomous and autonomous navigation systems. The aim of this work is to describe the project of an experimental platform for data acquisition in field for the study of the spatial variability and development of agricultural robotics technologies to operate in agricultural environments. The proposal is based on a systematization of scientific work to choose the design parameters utilized for the construction of the model. The kinematic study of the mechanical structure was made by the virtual prototyping process, based on modeling and simulating of the tension applied in frame, using the.