Optimization-Based Robotic Manipulation for Safe Interaction with Human Operators

This thesis investigates a method for human-robot interaction (HRI) in order to uphold productivity of industrial robots like minimization of the shortest operation time, while ensuring human safety like collision avoidance. For solving such problems an online motion planning approach for robotic manipulators with HRI has been proposed. The approach is based on model predictive control (MPC) with embedded mixed integer programming. The planning strategies of the robotic manipulators mainly considered in the thesis are directly performed in the workspace for easy obstacle representation. The non-convex optimization problem is approximated by a mixed-integer program (MIP). It is further effectively reformulated such that the number of binary variables and the number of feasible integer solutions are drastically decreased. Safety-relevant regions, which are potentially occupied by the human operators, can be generated online by a proposed method based on hidden Markov models. In contrast to previous approaches, which derive predictions based on probability density functions in the form of single points, such as most likely or expected human positions, the proposed method computes safety-relevant subsets of the workspace as a region which is possibly occupied by the human at future instances of time. The method is further enhanced by combining reachability analysis to increase the prediction accuracy. These safety-relevant regions can subsequently serve as safety constraints when the motion is planned by optimization. This way one arrives at motion plans that are safe, i.e. plans that avoid collision with a probability not less than a predefined threshold. The developed methods have been successfully applied to a developed demonstrator, where an industrial robot works in the same space as a human operator. The task of the industrial robot is to drive its end-effector according to a nominal sequence of grippingmotion-releasing operations while no collision with a human arm occurs.

Ameliorating the Overhead of Dynamic Optimization

Dynamic optimization has several key advantages. This includes the ability to work on binary code in the absence of sources and to perform optimization across module boundaries. However, it has a significant disadvantage viz-a-viz traditional static optimization: it has a significant runtime overhead. There can be performance gain only if the overhead can be amortized. In this paper, we will quantitatively analyze the runtime overhead introduced by a dynamic optimizer, DynamoRIO. We found that the major overhead does not come from the optimizer's operation. Instead, it comes from the extra code in the code cache added by DynamoRIO. After a detailed analysis, we will propose a method of trace construction that ameliorate the overhead introduced by the dynamic optimizer, thereby reducing the runtime overhead of DynamoRIO. We believe that the result of the study as well as the proposed solution is applicable to other scenarios such as dynamic code translation and managed execution that utilizes a framework similar to that of dynamic optimization.

Bit-Width Analysis for General Applications

It has been widely known that a significant part of the bits are useless or even unused during the program execution. Bit-width analysis targets at finding the minimum bits needed for each variable in the program, which ensures the execution correctness and resources saving. In this paper, we proposed a static analysis method for bit-widths in general applications, which approximates conservatively at compile time and is independent of runtime conditions. While most related work focus on integer applications, our method is also tailored and applicable to floating point variables, which could be extended to transform floating point number into fixed point numbers together with precision analysis. We used more precise representations for data value ranges of both scalar and array variables. Element level analysis is carried out for arrays. We also suggested an alternative for the standard fixed-point iterations in bi-directional range analysis. These techniques are implemented on the Trimaran compiler structure and tested on a set of benchmarks to show the results.

The curvature of the conical intersection seam: an approximate second-order analysis

We present a method for analyzing the curvature (second derivatives) of the conical intersection hyperline at an optimized critical point. Our method uses the projected Hessians of the degenerate states after elimination of the two branching space coordinates, and is equivalent to a frequency calculation on a single Born-Oppenheimer potential-energy surface. Based on the projected Hessians, we develop an equation for the energy as a function of a set of curvilinear coordinates where the degeneracy is preserved to second order (i.e., the conical intersection hyperline). The curvature of the potential-energy surface in these coordinates is the curvature of the conical intersection hyperline itself, and thus determines whether one has a minimum or saddle point on the hyperline. The equation used to classify optimized conical intersection points depends in a simple way on the first- and second-order degeneracy splittings calculated at these points. As an example, for fulvene, we show that the two optimized conical intersection points of C2v symmetry are saddle points on the intersection hyperline. Accordingly, there are further intersection points of lower energy, and one of C2 symmetry - presented here for the first time - is found to be the global minimum in the intersection space

Control and optimization of an SBR for nitrogen removal: from model calibration to plant operation

En aquesta tesis s'ha desenvolupat un sistema de control capaç d'optimitzar el funcionament dels Reactors Discontinus Seqüencials dins el camp de l'eliminació de matèria orgànica i nitrogen de les aigües residuals. El sistema de control permet ajustar en línia la durada de les etapes de reacció a partir de mesures directes o indirectes de sondes. En una primera etapa de la tesis s'ha estudiat la calibració de models matemàtics que permeten realitzar fàcilment provatures de diferents estratègies de control. A partir de l'anàlisis de dades històriques s'han plantejat diferents opcions per controlar l'SBR i les més convenients s'han provat mitjançant simulació. Després d'assegurar l'èxit de l'estratègia de control mitjançant simulacions s'ha implementat en una planta semi-industrial. Finalment es planteja l'estructura d'uns sistema supervisor encarregat de controlar el funcionament de l'SBR no només a nivell de fases sinó també a nivell cicle.

Analysing Google rankings through search engine optimization data

Purpose - The purpose of this paper is to identify the most popular techniques used to rank a web page highly in Google. Design/methodology/approach - The paper presents the results of a study into 50 highly optimized web pages that were created as part of a Search Engine Optimization competition. The study focuses on the most popular techniques that were used to rank highest in this competition, and includes an analysis on the use of PageRank, number of pages, number of in-links, domain age and the use of third party sites such as directories and social bookmarking sites. A separate study was made into 50 non-optimized web pages for comparison. Findings - The paper provides insight into the techniques that successful Search Engine Optimizers use to ensure a page ranks highly in Google. Recognizes the importance of PageRank and links as well as directories and social bookmarking sites. Research limitations/implications - Only the top 50 web sites for a specific query were analyzed. Analysing more web sites and comparing with similar studies in different competition would provide more concrete results. Practical implications - The paper offers a revealing insight into the techniques used by industry experts to rank highly in Google, and the success or other-wise of those techniques. Originality/value - This paper fulfils an identified need for web sites and e-commerce sites keen to attract a wider web audience.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Use of the statistical properties of the wavelet-transform coefficients for optimization of integration time in Fourier transform spectrometry

We show that an analysis of the mean and variance of discrete wavelet coefficients of coaveraged time-domain interferograms can be used as a specification for determining when to stop coaveraging. We also show that, if a prediction model built in the wavelet domain is used to determine the composition of unknown samples, a stopping criterion for the coaveraging process can be developed with respect to the uncertainty tolerated in the prediction.

Dynamic integrated system optimization and parameter estimation for discrete time optimal control of nonlinear systems

An algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimization and Parameter Estimation (DISOPE), which achieves the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimization procedure. A version of the algorithm with a linear-quadratic model-based problem, implemented in the C+ + programming language, is developed and applied to illustrative simulation examples. An analysis of the optimality and convergence properties of the algorithm is also presented.

Trade-offs and responsiveness of the single-layer urban canopy parametrization in WRF: an offline evaluation using the MOSCEM optimization algorithm and field observations

For an increasing number of applications, mesoscale modelling systems now aim to better represent urban areas. The complexity of processes resolved by urban parametrization schemes varies with the application. The concept of fitness-for-purpose is therefore critical for both the choice of parametrizations and the way in which the scheme should be evaluated. A systematic and objective model response analysis procedure (Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm) is used to assess the fitness of the single-layer urban canopy parametrization implemented in the Weather Research and Forecasting (WRF) model. The scheme is evaluated regarding its ability to simulate observed surface energy fluxes and the sensitivity to input parameters. Recent amendments are described, focussing on features which improve its applicability to numerical weather prediction, such as a reduced and physically more meaningful list of input parameters. The study shows a high sensitivity of the scheme to parameters characterizing roof properties in contrast to a low response to road-related ones. Problems in partitioning of energy between turbulent sensible and latent heat fluxes are also emphasized. Some initial guidelines to prioritize efforts to obtain urban land-cover class characteristics in WRF are provided. Copyright © 2010 Royal Meteorological Society and Crown Copyright.

Optimization of a sea ice model using basinwide observations of Arctic sea ice thickness, extent, and velocity

A stand-alone sea ice model is tuned and validated using satellite-derived, basinwide observations of sea ice thickness, extent, and velocity from the years 1993 to 2001. This is the first time that basin-scale measurements of sea ice thickness have been used for this purpose. The model is based on the CICE sea ice model code developed at the Los Alamos National Laboratory, with some minor modifications, and forcing consists of 40-yr ECMWF Re-Analysis (ERA-40) and Polar Exchange at the Sea Surface (POLES) data. Three parameters are varied in the tuning process: Ca, the air–ice drag coefficient; P*, the ice strength parameter; and α, the broadband albedo of cold bare ice, with the aim being to determine the subset of this three-dimensional parameter space that gives the best simultaneous agreement with observations with this forcing set. It is found that observations of sea ice extent and velocity alone are not sufficient to unambiguously tune the model, and that sea ice thickness measurements are necessary to locate a unique subset of parameter space in which simultaneous agreement is achieved with all three observational datasets.

Network optimization for enhanced resilience of urban heat island measurements

The urban heat island is a well-known phenomenon that impacts a wide variety of city operations. With greater availability of cheap meteorological sensors, it is possible to measure the spatial patterns of urban atmospheric characteristics with greater resolution. To develop robust and resilient networks, recognizing sensors may malfunction, it is important to know when measurement points are providing additional information and also the minimum number of sensors needed to provide spatial information for particular applications. Here we consider the example of temperature data, and the urban heat island, through analysis of a network of sensors in the Tokyo metropolitan area (Extended METROS). The effect of reducing observation points from an existing meteorological measurement network is considered, using random sampling and sampling with clustering. The results indicated the sampling with hierarchical clustering can yield similar temperature patterns with up to a 30% reduction in measurement sites in Tokyo. The methods presented have broader utility in evaluating the robustness and resilience of existing urban temperature networks and in how networks can be enhanced by new mobile and open data sources.

A model analysis of climate and CO2 controls on tree growth in a semi-arid woodland

We used a light-use efficiency model of photosynthesis coupled with a dynamic carbon allocation and tree-growth model to simulate annual growth of the gymnosperm Callitris columellaris in the semi-arid Great Western Woodlands, Western Australia, over the past 100 years. Parameter values were derived from independent observations except for sapwood specific respiration rate, fine-root turnover time, fine-root specific respiration rate and the ratio of fine-root mass to foliage area, which were estimated by Bayesian optimization. The model reproduced the general pattern of interannual variability in radial growth (tree-ring width), including the response to the shift in precipitation regimes that occurred in the 1960s. Simulated and observed responses to climate were consistent. Both showed a significant positive response of tree-ring width to total photosynthetically active radiation received and to the ratio of modeled actual to equilibrium evapotranspiration, and a significant negative response to vapour pressure deficit. However, the simulations showed an enhancement of radial growth in response to increasing atmospheric CO2 concentration (ppm) ([CO2]) during recent decades that is not present in the observations. The discrepancy disappeared when the model was recalibrated on successive 30-year windows. Then the ratio of fine-root mass to foliage area increases by 14% (from 0.127 to 0.144 kg C m-2) as [CO2] increased while the other three estimated parameters remained constant. The absence of a signal of increasing [CO2] has been noted in many tree-ring records, despite the enhancement of photosynthetic rates and water-use efficiency resulting from increasing [CO2]. Our simulations suggest that this behaviour could be explained as a consequence of a shift towards below-ground carbon allocation.

Study of optimization for cooperative motion between two humans in mutual tracking

Verbal communication is essential for human society and human civilization. Non-verbal communication, on the other hand, is more widely used not only by human but also other kind of animals, and the content of information is estimated even larger than the verbal communication. Among the non-verbal communication mutual motion is the simplest and easiest to study experimentally and analytically. We measured the power spectrum of the hand velocity in various conditions and clarified the following points on the feed-back and feed- forward mechanism as basic knowledge to understand the condition of good communication.