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Title: CHARACTERIZATION OF AN ADVANCED NEURON MODEL.
Creator: Echanique, Christopher, Behal, Aman, University of Central Florida
Abstract / Description: This thesis focuses on an adaptive quadratic spiking model of a motoneuron that is both versatile in its ability to represent a range of experimentally observed neuronal firing patterns as well as computationally efficient for large network simulation. The objective of research is to fit membrane voltage data to the model using a parameter estimation approach involving simulated annealing. By manipulating the system dynamics of the model, a realizable model with linear parameterization (LP)...
Show moreThis thesis focuses on an adaptive quadratic spiking model of a motoneuron that is both versatile in its ability to represent a range of experimentally observed neuronal firing patterns as well as computationally efficient for large network simulation. The objective of research is to fit membrane voltage data to the model using a parameter estimation approach involving simulated annealing. By manipulating the system dynamics of the model, a realizable model with linear parameterization (LP) can be obtained to simplify the estimation process. With a persistently excited current input applied to the model, simulated annealing is used to efficiently determine the best model parameters that minimize the square error function between the membrane voltage reference data and data generated by the LP model. Results obtained through simulation of this approach show feasibility to predict a range of different neuron firing patterns.
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Date Issued: 2012
Identifier: CFH0004259, ucf:44958
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH0004259

Title: NONLINEAR ESTIMATION AND CONTROL FOR ASSISTIVE ROBOTS.
Creator: Wang, Zhao, Behal, Aman, University of Central Florida
Abstract / Description: In this thesis, we document the progress in the estimation and control design of a smart assistive robot arm that can provide assistance during activities of daily living to the elderly and/or users with disabilities. Interaction with the environment is made challenging by the kinematic uncertainty in the robot, imperfect sensor calibration, limited view of angle as well as the fact that most activities of daily living are generally required to be performed in unstructured environments. For...
Show moreIn this thesis, we document the progress in the estimation and control design of a smart assistive robot arm that can provide assistance during activities of daily living to the elderly and/or users with disabilities. Interaction with the environment is made challenging by the kinematic uncertainty in the robot, imperfect sensor calibration, limited view of angle as well as the fact that most activities of daily living are generally required to be performed in unstructured environments. For monocular visual systems, range (or depth) information is always crucial for target modeling and system control design. In the first part of my thesis, a novel and effective method is developed to estimate the range information in perspective vision systems by observing the 2-D image information and known motion parameters. We have considered the presence of noise in the image space measurements and kinematic uncertainty in the motion parameters. Simulation and experiment results show the advantage of our algorithm in comparison with other approaches. In the second part of the thesis, Lyapunov-based design techniques are utilized to propose a 2.5D visual servoing controller which stabilizes the robot end-effector pose while satisfying practical constraints on the sensing and the actuation. First, a nominal feedback controller is introduced which is then modified through an optimization-based approach in order to satisfy the constraints related to limited camera field-of-view and size of actuation. In the absence of actuator constraints, the proposed control law yields semi-global asymptotic (exponential) stability. When actuator constraints are introduced, the result is local asymptotic stability with known bounds on the region of attraction. Simulation and experimental results demonstrate the effectiveness of the proposed control methodology.
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Date Issued: 2011
Identifier: CFE0003889, ucf:48755
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003889

Title: DATA-TRUE CHARACTERIZATION OF NEURONAL MODELS.
Creator: Suarez, Jose, Behal, Aman, University of Central Florida
Abstract / Description: In this thesis, a weighted least squares approach is initially presented to estimate the parameters of an adaptive quadratic neuronal model. By casting the discontinuities in the state variables at the spiking instants as an impulse train driving the system dynamics, the neuronal output is represented as a linearly parameterized model that depends on ltered versions of the input current and the output voltage at the cell membrane. A prediction errorbased weighted least squares method is...
Show moreIn this thesis, a weighted least squares approach is initially presented to estimate the parameters of an adaptive quadratic neuronal model. By casting the discontinuities in the state variables at the spiking instants as an impulse train driving the system dynamics, the neuronal output is represented as a linearly parameterized model that depends on ltered versions of the input current and the output voltage at the cell membrane. A prediction errorbased weighted least squares method is formulated for the model. This method allows for rapid estimation of model parameters under a persistently exciting input current injection. Simulation results show the feasibility of this approach to predict multiple neuronal ring patterns. Results of the method using data from a detailed ion-channel based model showed issues that served as the basis for the more robust resonate-and- re model presented. A second method is proposed to overcome some of the issues found in the adaptive quadratic model presented. The original quadratic model is replaced by a linear resonateand- re model -with stochastic threshold- that is both computational efficient and suitable for larger network simulations. The parameter estimation method presented here consists of different stages where the set of parameters is divided in to two. The rst set of parameters is assumed to represent the subthreshold dynamics of the model, and it is estimated using a nonlinear least squares algorithm, while the second set is associated with the threshold and reset parameters as its estimated using maximum likelihood formulations. The validity of the estimation method is then tested using detailed Hodgkin-Huxley model data as well as experimental voltage recordings from rat motoneurons.
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Date Issued: 2011
Identifier: CFE0003917, ucf:48724
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003917

Title: Lyapunov-Based Control Design for Uncertain MIMO Systems.
Creator: Wang, Zhao, Behal, Aman, Boloni, Ladislau, Haralambous, Michael, University of Central Florida
Abstract / Description: In this dissertation. we document the progress in the control design for a class of MIMO nonlinear uncertain system from five papers. In the first part, we address the problem of adaptive control design for a class of multi-input multi-output (MIMO)nonlinear systems. A Lypaunov based singularity free control law, which compensates for parametric uncertainty in both the drift vector and the input gain matrix, is proposed under the mild assumption that the signs of the leading minors of...
Show moreIn this dissertation. we document the progress in the control design for a class of MIMO nonlinear uncertain system from five papers. In the first part, we address the problem of adaptive control design for a class of multi-input multi-output (MIMO)nonlinear systems. A Lypaunov based singularity free control law, which compensates for parametric uncertainty in both the drift vector and the input gain matrix, is proposed under the mild assumption that the signs of the leading minors of thecontrol input gain matrix are known. Lyapunov analysis shows global uniform ultimate boundedness (GUUB) result for the tracking error under full state feedback (FSFB). Under the restriction that only the output vector is available for measurement, an output feedback (OFB) controller is designed based on a standard high gain observer (HGO) (-) stability under OFB is fostered by the uniformity of the FSFB solution. Simulation results for both FSFB and OFB controllers demonstrate the ef?cacy of the MIMO control design in the classical 2-DOF robot manipulator model.In the second part, an adaptive feedback control is designed for a class of MIMO nonlinear systems containing parametric uncertainty in both the drift vector and the input gain matrix, which is assumed to be full-rank and non-symmetric in general. Based on an SDU decomposition of the gain matrix, a singularity-free adaptive tracking control law is proposed that is shown to be globally asymptotically stable (GAS) under full-state feedback. Output feedback results are facilitated via the use of a high-gain observer (HGO). Under output feedback control, ultimate boundedness of the error signals is obtained (&)#241; the size of the bound is related to the size of the uncertainty in the parameters. An explicit upper bound is also provided on the size of the HGO gain constant.In third part, a class of aeroelastic systems with an unmodeled nonlinearity and external disturbance is considered. By using leading- and trailing-edge control surface actuations, a full-state feedforward/feedback controller is designed to suppress the aeroelastic vibrations of a nonlinear wing section subject to external disturbance. The full-state feedback control yields a uniformly ultimately bounded result for two-axis vibration suppression. With the restriction that only pitching and plunging displacements are measurable while their rates are not, a high-gain observer is used to modify the full-state feedback control design to an output feedback design. Simulation results demonstrate the ef ? cacy of the multi-input multi-output control toward suppressing aeroelastic vibration and limit cycle oscillations occurring in pre and post? utter velocity regimes when the system is subjected to a variety of external disturbance signals. Comparisons are drawn with a previously designed adaptive multi-input multi-output controller.In the fourth part, a continuous robust feedback control is designed for a class of high-order multi-input multi-output (MIMO) nonlinear systems with two degrees of freedom containing unstructured nonlinear uncertainties in the drift vector and parametric uncertainties in the high frequency gain matrix, which is allowed to be non-symmetric in general. Given some mild assumptions on the system model, a singularity-free continuous robust tracking control law is designed that is shown to be semi-globally asymptotically stable under full-state feedback through a Lyapunov stability analysis. The performance of the proposed algorithm have been verified on a two-link robot manipulator model and 2-DOF aeroelastic model.
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Date Issued: 2012
Identifier: CFE0004345, ucf:49420
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004345

Title: Smart Grasping using Laser and Tactile Array Sensors for UCF-MANUS- An Intelligent Assistive Robotic Manipulator.
Creator: Prakash, Kiran, Behal, Aman, Boloni, Ladislau, Haralambous, Michael, University of Central Florida
Abstract / Description: This thesis presents three improvements in the UCF MANUS Assistive Robotic Manipulator's grasping abilities. Firstly, the robot can now grasp objects that are deformable, heavy and have uneven contact surfaces without undergoing slippage during robotic operations, e.g. paper cup, filled water bottle. This is achieved by installing a high precision non-contacting Laser sensor1 that runs with an algorithm that processes raw-input data from the sensor, registers smallest variation in the...
Show moreThis thesis presents three improvements in the UCF MANUS Assistive Robotic Manipulator's grasping abilities. Firstly, the robot can now grasp objects that are deformable, heavy and have uneven contact surfaces without undergoing slippage during robotic operations, e.g. paper cup, filled water bottle. This is achieved by installing a high precision non-contacting Laser sensor1 that runs with an algorithm that processes raw-input data from the sensor, registers smallest variation in the relative position of the object with respect to the gripper. Secondly, the robot can grasp objects that are as light and small as single cereal grain without deforming it. To achieve this a MEMS Barometer based tactile sensor array device that can measure force that are as small as 1 gram equivalent is embedded into the gripper to enhance pressure sensing capabilities. Thirdly, the robot gripper gloves are designed aesthetically and conveniently to accommodate existing and newly added sensors using a 3D printing technology that uses light weight ABS plastic as a fabrication material. The newly designed system was experimented and found that a high degree of adaptability for different kinds of objects can be attained with a better performance than the previous system.
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Date Issued: 2016
Identifier: CFE0006164, ucf:51119
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006164

Title: General Vector Explicit - Impact Time and Angle Control Guidance.
Creator: Robinson, Loren, Qu, Zhihua, Behal, Aman, Xu, Yunjun, University of Central Florida
Abstract / Description: This thesis proposes and evaluates a new cooperative guidance law called General Vector Explicit -Impact Time and Angle Control Guidance (GENEX-ITACG). The motivation for GENEX-ITACGcame from an explicit trajectory shaping guidance law called General Vector Explicit Guidance(GENEX). GENEX simultaneously achieves design specifications on miss distance and terminalmissile approach angle while also providing a design parameter that adjusts the aggressiveness ofthis approach angle. Encouraged by...
Show moreThis thesis proposes and evaluates a new cooperative guidance law called General Vector Explicit -Impact Time and Angle Control Guidance (GENEX-ITACG). The motivation for GENEX-ITACGcame from an explicit trajectory shaping guidance law called General Vector Explicit Guidance(GENEX). GENEX simultaneously achieves design specifications on miss distance and terminalmissile approach angle while also providing a design parameter that adjusts the aggressiveness ofthis approach angle. Encouraged by the applicability of this user parameter, GENEX-ITACG is anextension that allows a salvo of missiles to cooperatively achieve the same objectives of GENEXagainst a stationary target through the incorporation of a cooperative trajectory shaping guidancelaw called Impact Time and Angle Control Guidance (ITACG).ITACG allows a salvo of missile to simultaneously hit a stationary target at a prescribed impactangle and impact time. This predetermined impact time is what allows each missile involvedin the salvo attack to simultaneously arrived at the target with unique approach angles, whichgreatly increases the probability of success against well defended targets. GENEX-ITACG furtherincreases this probability of kill by allowing each missile to approach the target with a uniqueapproach angle rate through the use of a user design parameter.The incorporation of ITACG into GENEX is accomplished through the use of linear optimal controlby casting the cost function of GENEX into the formulation of ITACG. The feasibility GENEXITACGis demonstrated across three scenarios that demonstrate the ITACG portion of the guidancelaw, the GENEX portion of the guidance law, and finally the entirety of the guidance law. Theresults indicate that GENEX-ITACG is able to successfully guide a salvo of missiles to simultaneouslyhit a stationary target at a predefined terminal impact angle and impact time, while alsoallowing the user to adjust the aggressiveness of approach.
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Date Issued: 2015
Identifier: CFE0005876, ucf:50868
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005876

Title: Coordinated Optimal Power Planning of Wind Turbines in an Offshore Wind Farm.
Creator: Vishwakarma, Puneet, Xu, Yunjun, Kapat, Jayanta, Kauffman, Jeffrey, Behal, Aman, University of Central Florida
Abstract / Description: Wind energy is on an upswing due to climate concerns and increasing energy demands on conventional sources. Wind energy is attractive and has the potential to dramatically reduce the dependency on non-renewable energy resources. With the increase in wind farms there is a need to improve the efficiency in power allocation and power generation among wind turbines. Wake interferences among wind turbines can lower the overall efficiency considerably, while offshore conditions pose increased...
Show moreWind energy is on an upswing due to climate concerns and increasing energy demands on conventional sources. Wind energy is attractive and has the potential to dramatically reduce the dependency on non-renewable energy resources. With the increase in wind farms there is a need to improve the efficiency in power allocation and power generation among wind turbines. Wake interferences among wind turbines can lower the overall efficiency considerably, while offshore conditions pose increased loading on wind turbines. In wind farms, wind turbines' wake affects each other depending on their positions and operation modes. Therefore it becomes essential to optimize the wind farm power production as a whole than to just focus on individual wind turbines. The work presented here develops a hierarchical power optimization algorithm for wind farms. The algorithm includes a cooperative level (or higher level) and an individual level (or lower level) for power coordination and planning in a wind farm. The higher level scheme formulates and solves a quadratic constrained programming problem to allocate power to wind turbines in the farm while considering the aerodynamic effect of the wake interaction among the turbines and the power generation capabilities of the wind turbines. In the lower level, optimization algorithm is based on a leader-follower structure driven by the local pursuit strategy. The local pursuit strategy connects the cooperative level power allocation and the individual level power generation in a leader-follower arrangement. The leader, could be a virtual entity and dictates the overall objective, while the followers are real wind turbines considering realistic constraints, such as tower deflection limits. A nonlinear wind turbine dynamics model is adopted for the low level study with loading and other constraints considered in the optimization. The stability of the algorithm in the low level is analyzed for the wind turbine angular velocity. Simulations are used to show the advantages of the method such as the ability to handle non-square input matrix, non-homogenous dynamics, and scalability in computational cost with rise in the number of wind turbines in the wind farm.
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Date Issued: 2015
Identifier: CFE0005899, ucf:50896
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005899

Title: Characterization of a Spiking Neuron Model via a Linear Approach.
Creator: Jabalameli, Amirhossein, Behal, Aman, Hickman, James, Haralambous, Michael, University of Central Florida
Abstract / Description: In the past decade, characterizing spiking neuron models has been extensively researched as anessential issue in computational neuroscience. In this thesis, we examine the estimation problemof two different neuron models. In Chapter 2, We propose a modified Izhikevich model withan adaptive threshold. In our two-stage estimation approach, a linear least squares method anda linear model of the threshold are derived to predict the location of neuronal spikes. However,desired results are not...
Show moreIn the past decade, characterizing spiking neuron models has been extensively researched as anessential issue in computational neuroscience. In this thesis, we examine the estimation problemof two different neuron models. In Chapter 2, We propose a modified Izhikevich model withan adaptive threshold. In our two-stage estimation approach, a linear least squares method anda linear model of the threshold are derived to predict the location of neuronal spikes. However,desired results are not obtained and the predicted model is unsuccessful in duplicating the spikelocations. Chapter 3 is focused on the parameter estimation problem of a multi-timescale adaptivethreshold (MAT) neuronal model. Using the dynamics of a non-resetting leaky integrator equippedwith an adaptive threshold, a constrained iterative linear least squares method is implemented tofit the model to the reference data. Through manipulation of the system dynamics, the thresholdvoltage can be obtained as a realizable model that is linear in the unknown parameters. This linearlyparametrized realizable model is then utilized inside a prediction error based framework to identifythe threshold parameters with the purpose of predicting single neuron precise firing times. Thisestimation scheme is evaluated using both synthetic data obtained from an exact model as well asthe experimental data obtained from in vitro rat somatosensory cortical neurons. Results show theability of this approach to fit the MAT model to different types of reference data.
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Date Issued: 2015
Identifier: CFE0005958, ucf:50803
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005958

Title: Modeling and Compensation for Efficient Human Robot Interaction.
Creator: Paperno, Nicholas, Behal, Aman, Haralambous, Michael, Boloni, Ladislau, University of Central Florida
Abstract / Description: The purpose of this research is to first: identify the important human factors to performance when operating an assistive robotic manipulator, second: develop a predictive model that will be able to determine a user's performance based on their known human factors, and third: develop compensators based on the determined important human factors that will help improve user performance and satisfaction. An extensive literature search led to the selection of ten potential human factors to be...
Show moreThe purpose of this research is to first: identify the important human factors to performance when operating an assistive robotic manipulator, second: develop a predictive model that will be able to determine a user's performance based on their known human factors, and third: develop compensators based on the determined important human factors that will help improve user performance and satisfaction. An extensive literature search led to the selection of ten potential human factors to be analyzed including reaction time, spatial abilities (orientation and visualization), working memory, visual perception, dexterity (gross and fine), depth perception, and visual acuity of both eyes (classified as strongest and weakest). 93 participants were recruited to perform six different pick-and-place and retrieval tasks using an assistive robotic device. During this time, a participants Time-on-Task, Number-of-Moves, and Number-of-Moves per minute were recorded. From this it was determined that all the human factors except visual perception were considered important to at least one aspect of a user's performance. Predictive models were then developed using random forest, linear models, and polynomial models. To compensate for deficiencies in certain human factors, the GUI was redesigned based on a heuristic analysis and user feedback. Multimodal feedback as well as adjustments in the sensitivity of the input device and reduction in the robot's speed of movement were also implemented. From a user study using 15 participants it was found that certain compensation did improve satisfaction of the users, particularly the multimodal feedback and sensitivity adjustment. The reduction of speed was met with mixed reviews from the participants.
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Date Issued: 2016
Identifier: CFE0006370, ucf:51504
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006370

Title: Individual Differences in Trust Toward Robotic Assistants.
Creator: Sanders, Tracy, Hancock, Peter, Mouloua, Mustapha, Szalma, James, Behal, Aman, University of Central Florida
Abstract / Description: This work on trust in human-robot interaction describes a series of three experiments from which a series of predictive models are developed. Previous work in trust and robotics has examined HRI components related to robots extensively, but there has been little research to quantify the influence of individual differences in trust on HRI. The present work seeks to fill that void by measuring individual differences across a variety of conditions, including differences in robot characteristics...
Show moreThis work on trust in human-robot interaction describes a series of three experiments from which a series of predictive models are developed. Previous work in trust and robotics has examined HRI components related to robots extensively, but there has been little research to quantify the influence of individual differences in trust on HRI. The present work seeks to fill that void by measuring individual differences across a variety of conditions, including differences in robot characteristics and environments. The models produced indicate that the main individual factors predicting trust in robotics include pre-existing attitudes towards robots, interpersonal trust, and personality traits.
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Date Issued: 2016
Identifier: CFE0006843, ucf:51776
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006843

Title: Learning to Grasp Unknown Objects using Weighted Random Forest Algorithm from Selective Image and Point Cloud Feature.
Creator: Iqbal, Md Shahriar, Behal, Aman, Boloni, Ladislau, Haralambous, Michael, University of Central Florida
Abstract / Description: This method demonstrates an approach to determine the best grasping location on an unknown object using Weighted Random Forest Algorithm. It used RGB-D value of an object as input to find a suitable rectangular grasping region as the output. To accomplish this task, it uses a subspace of most important features from a very high dimensional extensive feature space that contains both image and point cloud features. Usage of most important features in the grasping algorithm has enabled the...
Show moreThis method demonstrates an approach to determine the best grasping location on an unknown object using Weighted Random Forest Algorithm. It used RGB-D value of an object as input to find a suitable rectangular grasping region as the output. To accomplish this task, it uses a subspace of most important features from a very high dimensional extensive feature space that contains both image and point cloud features. Usage of most important features in the grasping algorithm has enabled the system to be computationally very fast while preserving maximum information gain. In this approach, the Random Forest operates using optimum parameters e.g. Number of Trees, Number of Features at each node, Information Gain Criteria etc. ensures optimization in learning, with highest possible accuracy in minimum time in an advanced practical setting. The Weighted Random Forest chosen over Support Vector Machine (SVM), Decision Tree and Adaboost for implementation of the grasping system outperforms the stated machine learning algorithms both in training and testing accuracy and other performance estimates. The Grasping System utilizing learning from a score function detects the rectangular grasping region after selecting the top rectangle that has the largest score. The system is implemented and tested in a Baxter Research Robot with Parallel Plate Gripper in action.
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Date Issued: 2014
Identifier: CFE0005509, ucf:50358
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005509

Title: Navigation of an Autonomous Differential Drive Robot for Field Scouting in Semi-structured Environments.
Creator: Freese, Douglas, Xu, Yunjun, Lin, Kuo-Chi, Kauffman, Jeffrey L., Behal, Aman, University of Central Florida
Abstract / Description: In recent years, the interests of introducing autonomous robots by growers into agriculture fields are rejuvenated due to the ever-increasing labor cost and the recent declining numbers of seasonal workers. The utilization of customized, autonomous agricultural robots has a profound impact on future orchard operations by providing low cost, meticulous inspection. Different sensors have been proven proficient in agrarian navigation including the likes of GPS, inertial, magnetic, rotary...
Show moreIn recent years, the interests of introducing autonomous robots by growers into agriculture fields are rejuvenated due to the ever-increasing labor cost and the recent declining numbers of seasonal workers. The utilization of customized, autonomous agricultural robots has a profound impact on future orchard operations by providing low cost, meticulous inspection. Different sensors have been proven proficient in agrarian navigation including the likes of GPS, inertial, magnetic, rotary encoding, time of flight as well as vision. To compensate for anticipated disturbances, variances and constraints contingent to the outdoor semi-structured environment, a differential style drive vehicle will be implemented as an easily controllable system to conduct tasks such as imaging and sampling.In order to verify the motion control of a robot, custom-designed for strawberry fields, the task is separated into multiple phases to manage the over-bed and cross-bed operation needs. In particular, during the cross-bed segment an elevated strawberry bed will provide distance references utilized in a logic filter and tuned PID algorithm for safe and efficient travel. Due to the significant sources of uncertainty such as wheel slip and the vehicle model, nonlinear robust controllers are designed for the cross-bed motion, purely relying on vision feedback. A simple image filter algorithm was developed for strawberry row detection, in which pixels corresponding to the bed center will be tracked while the vehicle is in controlled motion. This incorporated derivation and formulation of a bounded uncertainty parameter that will be employed in the nonlinear control. Simulation of the entire system was subsequently completed to ensure the control capability before successful validation in multiple commercial farms. It is anticipated that with the developed algorithms the authentication of fully autonomous robotic systems functioning in agricultural crops will provide heightened efficiency of needed costly services; scouting, disease detection, collection, and distribution.
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Date Issued: 2018
Identifier: CFE0007401, ucf:52743
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007401

Title: AUTONOMOUS ROBOTIC GRASPING IN UNSTRUCTURED ENVIRONMENTS.
Creator: Jabalameli, Amirhossein, Behal, Aman, Haralambous, Michael, Pourmohammadi Fallah, Yaser, Boloni, Ladislau, Xu, Yunjun, University of Central Florida
Abstract / Description: A crucial problem in robotics is interacting with known or novel objects in unstructured environments. While the convergence of a multitude of research advances is required to address this problem, our goal is to describe a framework that employs the robot's visual perception to identify and execute an appropriate grasp to pick and place novel objects. Analytical approaches explore for solutions through kinematic and dynamic formulations. On the other hand, data-driven methods retrieve grasps...
Show moreA crucial problem in robotics is interacting with known or novel objects in unstructured environments. While the convergence of a multitude of research advances is required to address this problem, our goal is to describe a framework that employs the robot's visual perception to identify and execute an appropriate grasp to pick and place novel objects. Analytical approaches explore for solutions through kinematic and dynamic formulations. On the other hand, data-driven methods retrieve grasps according to their prior knowledge of either the target object, human experience, or through information obtained from acquired data. In this dissertation, we propose a framework based on the supporting principle that potential contacting regions for a stable grasp can be foundby searching for (i) sharp discontinuities and (ii) regions of locally maximal principal curvature in the depth map. In addition to suggestions from empirical evidence, we discuss this principle by applying the concept of force-closure and wrench convexes. The key point is that no prior knowledge of objects is utilized in the grasp planning process; however, the obtained results show thatthe approach is capable to deal successfully with objects of different shapes and sizes. We believe that the proposed work is novel because the description of the visible portion of objects by theaforementioned edges appearing in the depth map facilitates the process of grasp set-point extraction in the same way as image processing methods with the focus on small-size 2D image areas rather than clustering and analyzing huge sets of 3D point-cloud coordinates. In fact, this approach dismisses reconstruction of objects. These features result in low computational costs and make it possible to run the proposed algorithm in real-time. Finally, the performance of the approach is successfully validated by applying it to the scenes with both single and multiple objects, in both simulation and real-world experiment setups.
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Date Issued: 2019
Identifier: CFE0007892, ucf:52757
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007892

Title: Value-of-Information based Data Collection in Underwater Sensor Networks.
Creator: Khan, Fahad, Turgut, Damla, Yuksel, Murat, Behal, Aman, Bassiouni, Mostafa, Garibay, Ivan, University of Central Florida
Abstract / Description: Underwater sensor networks are deployed in marine environments, presenting specific challenges compared to sensor networks deployed in terrestrial settings. Among the major issues that underwater sensor networks face is communication medium limitations that result in low bandwidth and long latency. This creates problems when these networks need to transmit large amounts of data over long distances. A possible solution to address this issue is to use mobile sinks such as autonomous underwater...
Show moreUnderwater sensor networks are deployed in marine environments, presenting specific challenges compared to sensor networks deployed in terrestrial settings. Among the major issues that underwater sensor networks face is communication medium limitations that result in low bandwidth and long latency. This creates problems when these networks need to transmit large amounts of data over long distances. A possible solution to address this issue is to use mobile sinks such as autonomous underwater vehicles (AUVs) to offload these large quantities of data. Such mobile sinks are called data mules. Often it is the case that a sensor network is deployed to report events that require immediate attention. Delays in reporting such events can have catastrophic consequences. In this dissertation, we present path planning algorithms that help in prioritizing data retrieval from sensor nodes in such a manner that nodes that require more immediate attention would be dealt with at the earliest. In other words, the goal is to improve the Quality of Information (QoI) retrieved. The path planning algorithms proposed in this dissertation are based on heuristics meant to improve the Value of Information (VoI) retrieved from a system. Value of information is a construct that helps in encoding the valuation of an information segment i.e. it is the price an optimal player would pay to obtain a segment of information in a game theoretic setting. Quality of information and value of information are complementary concepts. In this thesis, we formulate a value of information model for sensor networks and then consider the constraints that arise in underwater settings. On the basis of this, we develop a VoI-based path planning problem statement and propose heuristics that solve the path planning problem. We show through simulation studies that the proposed strategies improve the value, and hence, quality of the information retrieved. It is important to note that these path planning strategies can be applied equally well in terrestrial settings that deploy mobile sinks for data collection.
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Date Issued: 2019
Identifier: CFE0007476, ucf:52683
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007476

Title: Nonlinear Control Synthesis for Facilitation of Human-Robot Interaction.
Creator: Ding, Zhangchi, Behal, Aman, Pourmohammadi Fallah, Yaser, Haralambous, Michael, Boloni, Ladislau, Xu, Yunjun, University of Central Florida
Abstract / Description: Human-robot interaction is an area of interest that is becoming increasingly important in robotics research. Nonlinear control design techniques allow researchers to guarantee stability, performance, as well as safety, especially in cases involving physical human-robot interaction (PHRI). In this dissertation, we will propose two different nonlinear controllers and detail the design of an assistive robotic system to facilitate human-robot interaction. In Chapter 2, to facilitate physical...
Show moreHuman-robot interaction is an area of interest that is becoming increasingly important in robotics research. Nonlinear control design techniques allow researchers to guarantee stability, performance, as well as safety, especially in cases involving physical human-robot interaction (PHRI). In this dissertation, we will propose two different nonlinear controllers and detail the design of an assistive robotic system to facilitate human-robot interaction. In Chapter 2, to facilitate physical human-robot interaction, the problem of making a safe compliant contact between a human and an assistive robot is considered. Users with disabilities have a need to utilize their assistive robots for physical interaction during activities such as hair-grooming, scratching, face-sponging, etc. Specifically, we propose a hybrid force/velocity/attitude control for our physical human-robot interaction system which is based on measurements from a force/torque sensor mounted on the robot wrist. While automatically aligning the end-effector surface with the unknown environmental (human) surface, a desired commanded force is applied in the normal direction while following desired velocity commands in the tangential directions. A Lyapunov based stability analysis is provided to prove both convergence as well as passivity of the interaction to ensure both performance and safety. Simulation as well as experimental results verify the performance and robustness of the proposed hybrid force/velocity/attitude controller in the presence of dynamic uncertainties as well as safety compliance of human-robot interactions for a redundant robot manipulator.Chapter 3 presents the design, analysis, and experimental implementation of an adaptive control enabled intelligent algorithm to facilitate 1-click grasping of novel objects by a robotic gripper since one of the most common types of tasks for an assistive robot is pick and place/object retrieval tasks. But there are a variety of objects in our daily life all of which need different optimal force to grasp them. This algorithm facilitates automated grasping force adjustment. The use of object-geometry free modeling coupled with utilization of interaction force and slip velocity measurements allows for the design of an adaptive backstepping controller that is shown to be asymptotically stable via a Lyapunov-based analysis. Experiments with multiple objects using a prototype gripper with embedded sensing show that the proposed scheme is able to effectively immobilize novel objects within the gripper fingers. Furthermore, it is seen that the adaptation allows for close estimation of the minimum grasp force required for safe grasping which results in minimal deformation of the grasped object.In Chapter 4, we present the design and implementation of the motion controllerand adaptive interface for the second generation of the UCF-MANUSintelligent assistive robotic manipulator system. Based on usability testingfor the system, several features were implemented in the interface thatcould reduce the complexity of the human-robot interaction while alsocompensating for the deficits in different human factors, such as WorkingMemory, Response Inhibition, Processing Speed; , Depth Perception, SpatialAbility, Contrast Sensitivity. For the controller part, we designed severalnew features to provide the user has a less complex and safer interactionwith the robot, such as `One-click mode', `Move suggestion mode' and`Gripper Control Assistant'. As for the adaptive interface design, wedesigned and implemented compensators such as `Contrast Enhancement',`Object Proximity Velocity Reduction' and `Orientation Indicator'.
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Date Issued: 2019
Identifier: CFE0007798, ucf:52360
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007798

Title: Interactive Perception in Robotics.
Creator: Baghbahari Baghdadabad, Masoud, Behal, Aman, Haralambous, Michael, Lin, Mingjie, Sukthankar, Gita, Xu, Yunjun, University of Central Florida
Abstract / Description: Interactive perception is a significant and unique characteristic of embodied agents. An agent can discover plenty of knowledge through active interaction with its surrounding environment. Recently, deep learning structures introduced new possibilities to interactive perception in robotics. The advantage of deep learning is in acquiring self-organizing features from gathered data; however,it is computationally impractical to implement in real-time interaction applications. Moreover, it can be...
Show moreInteractive perception is a significant and unique characteristic of embodied agents. An agent can discover plenty of knowledge through active interaction with its surrounding environment. Recently, deep learning structures introduced new possibilities to interactive perception in robotics. The advantage of deep learning is in acquiring self-organizing features from gathered data; however,it is computationally impractical to implement in real-time interaction applications. Moreover, it can be difficult to attach a physical interpretation. An alternative suggested framework in such cases is integrated perception-action.In this dissertation, we propose two integrated interactive perception-action algorithms for real-time automated grasping of novel objects using pure tactile sensing. While visual sensing andprocessing is necessary for gross reaching movements, it can slow down the grasping process if it is the only sensing modality utilized. To overcome this issue, humans primarily utilize tactile perceptiononce the hand is in contact with the object. Inspired by this, we first propose an algorithm to define similar ability for a robot by formulating the required grasping steps.Next, we develop the algorithm to achieve force closure constraint via suggesting a human-like behavior for the robot to interactively identify the object. During this process, the robot adjuststhe hand through an interactive exploration of the object's local surface normal vector. After the robot finds the surface normal vector, it then tries to find the object edges to have a graspable finalrendezvous with the object. Such achievement is very important in order to find the objects edges for rectangular objects before fully grasping the object. We implement the proposed approacheson an assistive robot to demonstrate the performance of interactive perception-action strategies to accomplish grasping task in an automatic manner.
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Date Issued: 2019
Identifier: CFE0007780, ucf:52361
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007780

Title: Guided Autonomy for Quadcopter Photography.
Creator: Alabachi, Saif, Sukthankar, Gita, Behal, Aman, Lin, Mingjie, Boloni, Ladislau, Laviola II, Joseph, University of Central Florida
Abstract / Description: Photographing small objects with a quadcopter is non-trivial to perform with many common user interfaces, especially when it requires maneuvering an Unmanned Aerial Vehicle (C) to difficult angles in order to shoot high perspectives. The aim of this research is to employ machine learning to support better user interfaces for quadcopter photography. Human Robot Interaction (HRI) is supported by visual servoing, a specialized vision system for real-time object detection, and control policies...
Show morePhotographing small objects with a quadcopter is non-trivial to perform with many common user interfaces, especially when it requires maneuvering an Unmanned Aerial Vehicle (C) to difficult angles in order to shoot high perspectives. The aim of this research is to employ machine learning to support better user interfaces for quadcopter photography. Human Robot Interaction (HRI) is supported by visual servoing, a specialized vision system for real-time object detection, and control policies acquired through reinforcement learning (RL). Two investigations of guided autonomy were conducted. In the first, the user directed the quadcopter with a sketch based interface, and periods of user direction were interspersed with periods of autonomous flight. In the second, the user directs the quadcopter by taking a single photo with a handheld mobile device, and the quadcopter autonomously flies to the requested vantage point.This dissertation focuses on the following problems: 1) evaluating different user interface paradigms for dynamic photography in a GPS-denied environment; 2) learning better Convolutional Neural Network (CNN) object detection models to assure a higher precision in detecting human subjects than the currently available state-of-the-art fast models; 3) transferring learning from the Gazebo simulation into the real world; 4) learning robust control policies using deep reinforcement learning to maneuver the quadcopter to multiple shooting positions with minimal human interaction.
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Date Issued: 2019
Identifier: CFE0007774, ucf:52369
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007774

Title: Energy-optimal Guidance of an AUV Under Flow Uncertainty and Fluid-Particle Interaction.
Creator: De Zoysa Abeysiriwardena, Demuni Singith, Das, Tuhin, Kumar, Ranganathan, Elgohary, Tarek, Behal, Aman, University of Central Florida
Abstract / Description: The work presented gives an energy-optimal solution to the guidance problem of an AUV. The presented guidance methods are for lower level control of AUV paths, facilitating existing global planning methods to be carried out comparatively more efficiently. The underlying concept is to use the energy of fluid flow fields the AUVs are navigating to extend the duration of missions. This allows gathering of comparatively more data with higher spatio-temporal resolution. The problem is formulated...
Show moreThe work presented gives an energy-optimal solution to the guidance problem of an AUV. The presented guidance methods are for lower level control of AUV paths, facilitating existing global planning methods to be carried out comparatively more efficiently. The underlying concept is to use the energy of fluid flow fields the AUVs are navigating to extend the duration of missions. This allows gathering of comparatively more data with higher spatio-temporal resolution. The problem is formulated for a generalized two dimensional uniform flow field given a fixed final time andfree end states. This allows the AUVs to navigate to certain spatial positions while maintaining the required temporal resolution of each segment of its mission. The simplistic way the problem is posed allows an analytical closed form solution of the Euler-Lagrange equations. Two dimensional thrust vectors are obtained as optimal control inputs. The control inputs are then incorporated into afeedback structure, allowing the particle to navigate in the presence of disturbance in the flow field. Further, the work also explores the influence of fluid-particle interaction on the control cost and behavior of the particle. The concept of changing the cost weights of the optimal cost formulation in situ has been introduced. Potential applications of the present concept are explored through anobstacle avoidance scenario. The optimal guidance methods are then adapted to non-uniform flow fields with quadratic and discontinuous spatial variation being the primary focus.
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Date Issued: 2018
Identifier: CFE0007169, ucf:52282
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007169

Title: X-ray Radiation Enabled Cancer Detection and Treatment with Nanoparticles.
Creator: Hossain, Mainul, Su, Ming, Behal, Aman, Gong, Xun, Hu, Haiyan, Kapoor, Vikram, Deng, Weiwei, University of Central Florida
Abstract / Description: Despite significant improvements in medical sciences over the last decade, cancer still continues to be a major cause of death in humans throughout the world. Parallel to the efforts of understanding the intricacies of cancer biology, researchers are continuously striving to develop effective cancer detection and treatment strategies. Use of nanotechnology in the modern era opens up a wide range of possibilities for diagnostics, therapies and preventive measures for cancer management....
Show moreDespite significant improvements in medical sciences over the last decade, cancer still continues to be a major cause of death in humans throughout the world. Parallel to the efforts of understanding the intricacies of cancer biology, researchers are continuously striving to develop effective cancer detection and treatment strategies. Use of nanotechnology in the modern era opens up a wide range of possibilities for diagnostics, therapies and preventive measures for cancer management. Although, existing strategies of cancer detection and treatment, using nanoparticles, have been proven successful in case of cancer imaging and targeted drug deliveries, they are often limited by poor sensitivity, lack of specificity, complex sample preparation efforts and inherent toxicities associated with the nanoparticles, especially in case of in-vivo applications. Moreover, the detection of cancer is not necessarily integrated with treatment. X-rays have long been used in radiation therapy to kill cancer cells and also for imaging tumors inside the body using nanoparticles as contrast agents. However, X-rays, in combination with nanoparticles, can also be used for cancer diagnosis by detecting cancer biomarkers and circulating tumor cells. Moreover, the use of nanoparticles can also enhance the efficacy of X-ray radiation therapy for cancer treatment.This dissertation describes a novel in vitro technique for cancer detection and treatment using X-ray radiation and nanoparticles. Surfaces of synthesized metallic nanoparticles have been modified with appropriate ligands to specifically target cancer cells and biomarkers in vitro. Characteristic X-ray fluorescence signals from the X-ray irradiated nanoparticles are then used for detecting the presence of cancer. The method enables simultaneous detection of multiple cancer biomarkers allowing accurate diagnosis and early detection of cancer. Circulating tumor cells, which are the primary indicators of cancer metastasis, have also been detected where the use of magnetic nanoparticles allows enrichment of rare cancer cells prior to detection. The approach is unique in that it integrates cancer detection and treatment under one platform, since, X-rays have been shown to effectively kill cancer cells through radiation induced DNA damage. Due to high penetrating power of X-rays, the method has potential applications for in vivo detection and treatment of deeply buried cancers in humans. The effect of nanoparticle toxicity on multiple cell types has been investigated using conventional cytotoxicity assays for both unmodified nanoparticles as well as nanoparticles modified with a variety of surface coatings. Appropriate surface modifications have significantly reduced inherent toxicity of nanoparticles, providing possibilities for future clinical applications. To investigate cellular damages caused by X-ray radiation, an on-chip biodosimeter has been fabricated based on three dimensional microtissues which allows direct monitoring of responses to X-ray exposure for multiple mammalian cell types. Damage to tumor cells caused by X-rays is known to be significantly higher in presence of nanoparticles which act as radiosensitizers and enhance localized radiation doses. An analytical approach is used to investigate the various parameters that affect the radiosensitizing properties of the nanoparticles. The results can be used to increase the efficacy of nanoparticle aided X-ray radiation therapy for cancer treatment by appropriate choice of X-ray beam energy, nanoparticle size, material composition and location of nanoparticle with respect to the tumor cell nucleus.
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Date Issued: 2012
Identifier: CFE0004547, ucf:49242
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004547

Title: Modeling Transport and Protein Adsorption in Microfluidic Systems.
Creator: Finch, Craig, Hickman, James, Kincaid, John, Lin, Kuo-Chi, Behal, Aman, Cho, Hyoung, University of Central Florida
Abstract / Description: This work describes theoretical advances in the modeling and simulation of microfluidic systems and demonstrates the practical application of those techniques. A new multi-scale model of the adsorption of hard spheres was formulated to bridge the gap between simulations of discrete particles and continuum fluid dynamics. A whispering gallery mode (WGM) biosensor was constructed and used to measure the kinetics of adsorption for two types of proteins on four different surfaces. Computational...
Show moreThis work describes theoretical advances in the modeling and simulation of microfluidic systems and demonstrates the practical application of those techniques. A new multi-scale model of the adsorption of hard spheres was formulated to bridge the gap between simulations of discrete particles and continuum fluid dynamics. A whispering gallery mode (WGM) biosensor was constructed and used to measure the kinetics of adsorption for two types of proteins on four different surfaces. Computational fluid dynamics was used to analyze the transport of proteins in the flow cell of the biosensor. Kinetic models of protein adsorption that take transport limitations into account were fitted to the experimental data and used to draw conclusions about the mechanisms of adsorption. Transport simulations were then applied to the practical problem of optimizing the design of a microfluidic bioreactor to enable (")plugs(") of fluid to flow from one chamber to the next with minimal dispersion. Experiments were used to validate the transport simulations. The combination of quantitative modeling and simulation and experiments led to results that could not have been achieved using either approach by itself. Simulation tools that accurately predict transport and protein adsorption will enable the rational design of microfluidic devices for biomedical applications.
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Date Issued: 2011
Identifier: CFE0004474, ucf:49313
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004474

Control (3)	+ -
Robotics (3)	+ -
AUV (2)	+ -
Adaptive Control (2)	+ -
Assistive Robotics (2)	+ -

Grasping (2)	+ -
Nonlinear Control (2)	+ -
Pinning Control (2)	+ -
human-robot interaction (2)	+ -
optimization (2)	+ -
parameter estimation (2)	+ -
simulation (2)	+ -
Adaboost (1)	+ -
Adaptive (1)	+ -
Agent system (1)	+ -
Agriculture (1)	+ -
Algorithms (1)	+ -
Amplitude Estimation (1)	+ -
Assistive Robotic (1)	+ -
Autonomous (1)	+ -
Autonomous ground vehicle (1)	+ -
Baxter Research Robot (1)	+ -
Behavior recognition (1)	+ -
Bio-inspired method (1)	+ -
Chaos (1)	+ -
Chaotic Oscillators (1)	+ -
Complex Networks (1)	+ -
Computer Vision (1)	+ -
Cooperative Control (1)	+ -
Crowd modeling (1)	+ -

ETD Collection (32)	+ -
Honors Collection (1)	+ -

Behal, Aman (33)	+ -
University of Central Florida (33)	+ -
Haralambous, Michael (12)	+ -
Boloni, Ladislau (10)	+ -
Xu, Yunjun (8)	+ -

Sukthankar, Gita (5)	+ -
Hickman, James (3)	+ -
Lin, Kuo-Chi (3)	+ -
Mikhael, Wasfy (3)	+ -
Qu, Zhihua (3)	+ -
Atia, George (2)	+ -
Das, Tuhin (2)	+ -
Garibay, Ivan (2)	+ -
Jabalameli, Amirhossein (2)	+ -
Kutkut, Nasser (2)	+ -
Lin, Mingjie (2)	+ -
Paperno, Nicholas (2)	+ -
Pourmohammadi Fallah, Yaser (2)	+ -
Turgut, Damla (2)	+ -
Wang, Zhao (2)	+ -
Alabachi, Saif (1)	+ -
Alhosani, Mohamed (1)	+ -
Baghbahari Baghdadabad, Masoud (1)	+ -
Bai, Yuanli (1)	+ -
Bassiouni, Mostafa (1)	+ -
Batarseh, Issa (1)	+ -
Catbas, Fikret (1)	+ -
Chen, Lin (1)	+ -
Cho, Hyoung (1)	+ -
Cooper, Douglas (1)	+ -

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