Deep Forward and Inverse Perceptual Models for Tracking and Prediction

We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics. Specifically, we present a perceptual model for generating video frames from state with deep networks, and provide a framework for its use in tracking and prediction tasks. We show that our proposed model greatly outperforms standard deconvolutional methods and GANs for image generation, producing clear, photo-realistic images. We also develop a convolutional neural network model for state estimation and compare the result to an Extended Kalman Filter to estimate robot trajectories. We validate all models on a real robotic system.Comment: 8 pages, International Conference on Robotics and Automation (ICRA) 201

Develop an autonomous product-based reconfigurable manufacturing system

With the ever-emerging market including mass customization and product variety, reconfigurable manufacturing systems (RMS) have been presented as the solution. A manufacturing system that combines the benefits of the two classic manufacturing systems to increase responsiveness and reduce production time and costs. To cope with the lack of physical systems, an RMS system have been built at UiT Narvik. Today, both reconfiguration and deciding layout must be executed manually by a human. A task that is both incredibly time consuming and far from optimal. A method of automating the layout generation and thus the manufacturing system is presented in this thesis. To the author’s knowledge such experiment has not been performed previously. Layouts is generated with a NSGA-II algorithm in Python by minimizing objectives from a developed mathematical model. The results have been tested with a MiR-100 mobile robot placing five modules in two different layouts. The results have been compared with a digital visualization for validation. In addition to the visualization, videos of the physical system's automated layout generation are presented. The results concludes that the method both generates feasible layouts as well as enhancing the automation of the system

Longitudinal flying qualities criteria for single-pilot instrument flight operations

Modern estimation and control theory, flight testing, and statistical analysis were used to deduce flying qualities criteria for General Aviation Single Pilot Instrument Flight Rule (SPIFR) operations. The principal concern is that unsatisfactory aircraft dynamic response combined with high navigation/communication workload can produce problems of safety and efficiency. To alleviate these problems. The relative importance of these factors must be determined. This objective was achieved by flying SPIFR tasks with different aircraft dynamic configurations and assessing the effects of such variations under these conditions. The experimental results yielded quantitative indicators of pilot's performance and workload, and for each of them, multivariate regression was applied to evaluate several candidate flying qualities criteria

Application of the genetic algorithm to an ecological simulation

A computational framework is built and demonstrated which is capable of testing plant growth strategies. The framework consists of Vgrass, a carbon based simulation model of a single Zostera marina plant, and the genetic algorithm (GA). Vgrass is based on published seagrass models, published photosynthetic data, and general plant physiology information. The model grows individual leaves whose initiation times are based on degree-day intervals. Leaf size is computed and combined with shoot density to compute population self shading. Leaf length is an emergent property since leaf growth is limited by light attenuation caused by self shading. The model is able to show the relationship between leaf size and shoot density in response to light availability. Degree-days is also shown to be an effective method in modeling the emergence of Zostera marina leaves. The GA and Vgrass are combined to demonstrate the GA as an optimization method and to demonstrate a secondary sensitivity analysis. In an optimization exercise, the RMS error between Vgrass biomass and that of another published model is minimized. Solutions with fitness ranking within 10% of the smallest RMS error are compared in a secondary sensitivity analysis. The analysis can be used to indicate parameter sensitivity in regards to the models ability to attain the optimization goal. Plant growth strategies are tested by searching for configurations of Vgrass parameters best able to: maximize relative growth rate, maximize biomass, and maximize net primary production. Configurations found by the GA lead to plant growth patterns that are not biologically realistic; plant growth strategies based on maximizing growth lead to unrealistic plant growth. The plant growth patterns from each of the tests are discussed in relation to ecological and economic principles. Configurations found by the GA search are unique to the optimization goal and the resulting plant growth patterns are shown to support the given goal. Therefore, the computational framework is shown to be successful in testing plant growth strategies. Further, this study shows that care must be taken when defining the fitness function and that the GA is and that the GA is an effective tool at finding holes in a model

Ka-band Ga-As FET noise receiver/device development

The development of technology for a 30 GHz low noise receiver utilizing GaAs FET devices exclusively is discussed. This program required single and dual-gate FET devices, low noise FET amplifiers, dual-gate FET mixers, and FET oscillators operating at Ka-band frequencies. A 0.25 micrometer gate FET device, developed with a minimum noise figure of 3.3 dB at 29 GHz and an associated gain of 7.4 dB, was used to fabricate a 3-stage amplifier with a minimum noise figure and associated gain of 4.4 dB and 17 dB, respectively. The 1-dB gain bandwidth of this amplifier extended from below 26.5 GHz to 30.5 GHz. A dual-gate mixer with a 2 dB conversion loss and a minimum noise figure of 10 dB at 29 GHz as well as a dielectric resonator stabilized FET oscillator at 25 GHz for the receiver L0. From these components, a hybrid microwave integrated circuit receiver was constructed which demonstrates a minimum single-side band noise figure of 4.6 dB at 29 GHz with a conversion gain of 17 dB. The output power at the 1-dB gain compression point was -5 dBm

SCAR arrow-wing active flutter suppression system

The potential performance and direct operating cost benefits of an active flutter suppression system (FSS) for the NASA arrow-wing supersonic cruise configuration were determined. A FSS designed to increase the flutter speed of the baseline airplane 20 percent. A comparison was made of the performance and direct operating cost between the FSS equipped aircraft and a previously defined configuration with structural modifications to provide the same flutter speed. Control system synthesis and evaluation indicated that a FSS could provide the increase in flutter speed without degrading airplane reliability, safety, handling qualities, or ride quality, and without increasing repeated loads or hydraulic and electrical power capacity requirements

FITTING A PARAMETRIC MODEL TO A CLOUD OF POINTS VIA OPTIMIZATION METHODS

Computer Aided Design (CAD) is a powerful tool for designing parametric geometry. However, many CAD models of current configurations are constructed in previous generations of CAD systems, which represent the configuration simply as a collection of surfaces instead of as a parametrized solid model. But since many modern analysis techniques take advantage of a parametrization, one often has to re-engineer the configuration into a parametric model. The objective here is to generate an efficient, robust, and accurate method for fitting parametric models to a cloud of points. The process uses a gradient-based optimization technique, which is applied to the whole cloud, without the need to segment or classify the points in the cloud a priori. First, for the points associated with any component, a variant of the Levenberg-Marquardt gradient-based optimization method (ILM) is used to find the set of model parameters that minimizes the least-square errors between the model and the points. The efficiency of the ILM algorithm is greatly improved through the use of analytic geometric sensitivities and sparse matrix techniques. Second, for cases in which one does not know a priori the correspondences between points in the cloud and the geometry model\u27s components, an efficient initialization and classification algorithm is introduced. While this technique works well once the configuration is close enough, it occasionally fails when the initial parametrized configuration is too far from the cloud of points. To circumvent this problem, the objective function is modified, which has yielded good results for all cases tested. This technique is applied to a series of increasingly complex configurations. The final configuration represents a full transport aircraft configuration, with a wing, fuselage, empennage, and engines. Although only applied to aerospace applications, the technique is general enough to be applicable in any domain for which basic parametrized models are available

Display/control requirements for automated VTOL aircraft

A systematic design methodology for pilot displays in advanced commercial VTOL aircraft was developed and refined. The analyst is provided with a step-by-step procedure for conducting conceptual display/control configurations evaluations for simultaneous monitoring and control pilot tasks. The approach consists of three phases: formulation of information requirements, configuration evaluation, and system selection. Both the monitoring and control performance models are based upon the optimal control model of the human operator. Extensions to the conventional optimal control model required in the display design methodology include explicit optimization of control/monitoring attention; simultaneous monitoring and control performance predictions; and indifference threshold effects. The methodology was applied to NASA's experimental CH-47 helicopter in support of the VALT program. The CH-47 application examined the system performance of six flight conditions. Four candidate configurations are suggested for evaluation in pilot-in-the-loop simulations and eventual flight tests

Millimeter-long Fiber Fabry-Perot cavities

We demonstrate fiber Fabry-Perot (FFP) cavities with concave mirrors that can be operated at cavity lengths as large as 1.5mm without significant deterioration of the finesse. This is achieved by using a laser dot machining technique to shape spherical mirrors with ultralow roughness and employing single-mode fibers with large mode area for good mode matching to the cavity. Additionally, in contrast to previous FFPs, these cavities can be used over an octave-spanning frequency range with adequate coatings. We also show directly that shape deviations caused by the fiber's index profile lead to a finesse decrease as observed in earlier attempts to build long FFP cavities, and show a way to overcome this problem