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Title: Six Degree of Freedom Dynamic Modeling of a High Altitude Airship and Its Trajectory Optimization Using Direct Collocation Method.
Creator: Pierre-Louis, Pradens, Xu, Yunjun, Lin, Kuo-Chi, Das, Tuhin, University of Central Florida
Abstract / Description: The long duration airborne feature of airships makes them an attractive solution for many military and civil applications such as long-endurance surveillance, reconnaissance, environment monitoring, communication utilities, and energy harvesting. To achieve a minimum energy periodic motion in the air, an optimal trajectory problem is solved using basic direct collocation methods. In the direct approach, the optimal control problem is converted into a nonlinear programming (NLP). Pseudo...
Show moreThe long duration airborne feature of airships makes them an attractive solution for many military and civil applications such as long-endurance surveillance, reconnaissance, environment monitoring, communication utilities, and energy harvesting. To achieve a minimum energy periodic motion in the air, an optimal trajectory problem is solved using basic direct collocation methods. In the direct approach, the optimal control problem is converted into a nonlinear programming (NLP). Pseudo-inverse and several discretization methods such as Trapezoidal and Hermite-Simpson are used to obtain a numerical approximated solution by discretizing the states and controls into a set of equal nodes. These nodes are approximated by a cubic polynomial function which makes it easier for the optimization to converge while ensuring the problem constraints and the equations of motion are satisfied at the collocation points for a defined trajectory. In this study, direct collocation method provides the ability to obtain an approximation solution of the minimum energy expenditure of a very complex dynamic problem using Matlab fmincon optimization algorithm without using Himiltonian function with Lagrange multipliers. The minimal energy trajectory of the airship is discussed and results are presented.
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Date Issued: 2017
Identifier: CFE0006779, ucf:51822
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006779

Title: REAL-TIME TRAJECTORY PLANNING FOR GROUNDAND AERIAL VEHICLES IN A DYNAMIC ENVIRONMENT.
Creator: Yang, Jian, Qu, Zhihua, University of Central Florida
Abstract / Description: In this dissertation, a novel and generic solution of trajectory generation is developed and evaluated for ground and aerial vehicles in a dynamic environment. By explicitly considering a kinematic model of the ground vehicles, the family of feasible trajectories and their corresponding steering controls are derived in a closed form and are expressed in terms of one adjustable parameter for the purpose of collision avoidance. A collision-avoidance condition is developed for the dynamically...
Show moreIn this dissertation, a novel and generic solution of trajectory generation is developed and evaluated for ground and aerial vehicles in a dynamic environment. By explicitly considering a kinematic model of the ground vehicles, the family of feasible trajectories and their corresponding steering controls are derived in a closed form and are expressed in terms of one adjustable parameter for the purpose of collision avoidance. A collision-avoidance condition is developed for the dynamically changing environment, which consists of a time criterion and a geometrical criterion. By imposing this condition, one can determine a family of collision-free paths in a closed form. Then, optimization problems with respect to different performance indices are setup to obtain optimal solutions from the feasible trajectories. Among these solutions, one with respect to the near-shortest distance and another with respect to the near-minimal control energy are analytical and simple. These properties make them good choices for real-time trajectory planning. Such optimal paths meet all boundary conditions, are twice differentiable, and can be updated in real time once a change in the environment is detected. Then this novel method is extended to 3D space to find a real-time optimal path for aerial vehicles. After that, to reflect the real applications, obstacles are classified to two types: "hard" obstacles that must be avoided, and "soft" obstacles that can be run over/through. Moreover, without losing generality, avoidance criteria are extended to obstacles with any geometric shapes. This dissertation also points out that the emphases of the future work are to consider other constraints such as the bounded velocity and so on. The proposed method is illustrated by computer simulations.
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Date Issued: 2008
Identifier: CFE0002031, ucf:47594
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002031

Title: Bio-inspired, Varying Manifold Based Method with Enhanced Initial Guess Strategies for Single Vehicle's Optimal Trajectory Planning.
Creator: Li, Ni, Xu, Yunjun, Lin, Kuo-Chi, Bai, Yuanli, Behal, Aman, University of Central Florida
Abstract / Description: Trajectory planning is important in many applications involving unmanned aerial vehicles, underwater vehicles, spacecraft, and industrial manipulators. It is still a challenging task to rapidly find an optimal trajectory while taking into account dynamic and environmental constraints. In this dissertation, a unified, varying manifold based optimal trajectory planning method inspired by several predator-prey relationships is investigated to tackle this challenging problem. Biological species,...
Show moreTrajectory planning is important in many applications involving unmanned aerial vehicles, underwater vehicles, spacecraft, and industrial manipulators. It is still a challenging task to rapidly find an optimal trajectory while taking into account dynamic and environmental constraints. In this dissertation, a unified, varying manifold based optimal trajectory planning method inspired by several predator-prey relationships is investigated to tackle this challenging problem. Biological species, such as hoverflies, ants, and bats, have developed many efficient hunting strategies. It is hypothesized that these types of predators only move along paths in a carefully selected manifold based on the prey's motion in some of their hunting activities. Inspired by these studies, the predator-prey relationships are organized into a unified form and incorporated into the trajectory optimization formulation, which can reduce the computational cost in solving nonlinear constrained optimal trajectory planning problems. Specifically, three motion strategies are studied in this dissertation: motion camouflage, constant absolute target direction, and local pursuit. Necessary conditions based on the speed and obstacle avoidance constraints are derived. Strategies to tune initial guesses are proposed based on these necessary conditions to enhance the convergence rate and reduce the computational cost of the motion camouflage inspired strategy. The following simulations have been conducted to show the advantages of the proposed methods: a supersonic aircraft minimum-time-to-climb problem, a ground robot obstacle avoidance problem, and a micro air vehicle minimum time trajectory problem. The results show that the proposed methods can find the optimal solution with higher success rate and faster convergent speed as compared with some other popular methods. Among these three motion strategies, the method based on the local pursuit strategy has a relatively higher success rate when compared to the other two.In addition, the optimal trajectory planning method is embedded into a receding horizon framework with unknown parameters updated in each planning horizon using an Extended Kalman Filter.
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Date Issued: 2013
Identifier: CFE0005023, ucf:49986
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005023

Title: CONTROL OF NONHOLONOMIC SYSTEMS.
Creator: Yuan, Hongliang, Qu, Zhihua, University of Central Florida
Abstract / Description: Many real-world electrical and mechanical systems have velocity-dependent constraints in their dynamic models. For example, car-like robots, unmanned aerial vehicles, autonomous underwater vehicles and hopping robots, etc. Most of these systems can be transformed into a chained form, which is considered as a canonical form of these nonholonomic systems. Hence, study of chained systems ensure their wide applicability. This thesis studied the problem of continuous feed-back control of the...
Show moreMany real-world electrical and mechanical systems have velocity-dependent constraints in their dynamic models. For example, car-like robots, unmanned aerial vehicles, autonomous underwater vehicles and hopping robots, etc. Most of these systems can be transformed into a chained form, which is considered as a canonical form of these nonholonomic systems. Hence, study of chained systems ensure their wide applicability. This thesis studied the problem of continuous feed-back control of the chained systems while pursuing inverse optimality and exponential convergence rates, as well as the feed-back stabilization problem under input saturation constraints. These studies are based on global singularity-free state transformations and controls are synthesized from resulting linear systems. Then, the application of optimal motion planning and dynamic tracking control of nonholonomic autonomous underwater vehicles is considered. The obtained trajectories satisfy the boundary conditions and the vehicles' kinematic model, hence it is smooth and feasible. A collision avoidance criteria is set up to handle the dynamic environments. The resulting controls are in closed forms and suitable for real-time implementations. Further, dynamic tracking controls are developed through the Lyapunov second method and back-stepping technique based on a NPS AUV II model. In what follows, the application of cooperative surveillance and formation control of a group of nonholonomic robots is investigated. A designing scheme is proposed to achieves a rigid formation along a circular trajectory or any arbitrary trajectories. The controllers are decentralized and are able to avoid internal and external collisions. Computer simulations are provided to verify the effectiveness of these designs.
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Date Issued: 2009
Identifier: CFE0002683, ucf:48220
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002683

Title: Bio-Inspired Visual Servo Control of a Picking Mechanism in an Agricultural Ground Robot.
Creator: Defterli, Sinem, Xu, Yunjun, Kauffman, Jeffrey L., Lin, Kuo-Chi, Song, Sang-Eun, Zheng, Qipeng, University of Central Florida
Abstract / Description: For a recently constructed disease detection agricultural ground robot, the segregation of unhealthy leaves fromstrawberry plants is a major task of the robot's manipulation subsystem in field operations. In this dissertation, the motion planning of a custom-designedpicking mechanism in the ground robot's subsystem is studied in two sections. First, a set of analytical, suboptimal semi-analyticaland numerical algorithms are studied to solve the inverse kinematics problem of the handling...
Show moreFor a recently constructed disease detection agricultural ground robot, the segregation of unhealthy leaves fromstrawberry plants is a major task of the robot's manipulation subsystem in field operations. In this dissertation, the motion planning of a custom-designedpicking mechanism in the ground robot's subsystem is studied in two sections. First, a set of analytical, suboptimal semi-analyticaland numerical algorithms are studied to solve the inverse kinematics problem of the handling mechanism in firmcircumstances. These premeditated approaches are built on the computation of the joint variables by an identified 3Dposition data of the target leaf only. The outcomes of the three solution algorithms are evaluated in terms of the performanceindexes of energy change and the CPU time cost. The resultant postures of the mechanism for different target pointlocations are observed both in simulations and the hardware experiments with each IK solution. Secondly, after the manipulation task of the mechanism via the proposed inverse kinematicalgorithms is performed, some compensation may be needed due to the sudden and unpredicted deviation of the targetposition under field conditions.For the purpose of finding optimal joint values under certain constraints, a trajectory optimization problem in image-based visual servoing method via the camera-in-handconfiguration is initiated when the end-effector is in the close proximity of the target leaf. In this part of the study, a bio-inspired trajectory optimization problem in image-basedvisual servoing method is constructed based on the mathematical model derived from the prey-predatorrelationships in nature. In this biological phenomenon, the predator constructs its path in a certain subspace whilecatching the prey. When this motion strategy is applied to trajectory optimization problems, it causes a significantreduce in the computation cost since it finds the optimum solution in a certain manifold. The performance of the introducedbio-inspired trajectory optimization in visual servoing is validated with the hardware experiments both in laboratory settings and in fieldconditions.
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Date Issued: 2018
Identifier: CFE0007170, ucf:52247
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007170

Title: Vision-Based Testbeds for Control System Applicaitons.
Creator: Sivilli, Robert, Xu, Yunjun, Gou, Jihua, Cho, Hyoung, Pham, Khanh, University of Central Florida
Abstract / Description: In the field of control systems, testbeds are a pivotal step in the validation and improvement of new algorithms for different applications. They provide a safe, controlled environment typically having a significantly lower cost of failure than the final application. Vision systems provide nonintrusive methods of measurement that can be easily implemented for various setups and applications. This work presents methods for modeling, removing distortion, calibrating, and rectifying single and...
Show moreIn the field of control systems, testbeds are a pivotal step in the validation and improvement of new algorithms for different applications. They provide a safe, controlled environment typically having a significantly lower cost of failure than the final application. Vision systems provide nonintrusive methods of measurement that can be easily implemented for various setups and applications. This work presents methods for modeling, removing distortion, calibrating, and rectifying single and two camera systems, as well as, two very different applications of vision-based control system testbeds: deflection control of shape memory polymers and trajectory planning for mobile robots. First, a testbed for the modeling and control of shape memory polymers (SMP) is designed. Red-green-blue (RGB) thresholding is used to assist in the webcam-based, 3D reconstruction of points of interest. A PID based controller is designed and shown to work with SMP samples, while state space models were identified from step input responses. Models were used to develop a linear quadratic regulator that is shown to work in simulation. Also, a simple to use graphical interface is designed for fast and simple testing of a series of samples. Second, a robot testbed is designed to test new trajectory planning algorithms. A template-based predictive search algorithm is investigated to process the images obtained through a low-cost webcam vision system, which is used to monitor the testbed environment. Also a user-friendly graphical interface is developed such that the functionalities of the webcam, robots, and optimizations are automated. The testbeds are used to demonstrate a wavefront-enhanced, B-spline augmented virtual motion camouflage algorithm for single or multiple robots to navigate through an obstacle dense and changing environment, while considering inter-vehicle conflicts, obstacle avoidance, nonlinear dynamics, and different constraints. In addition, it is expected that this testbed can be used to test different vehicle motion planning and control algorithms.
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Date Issued: 2012
Identifier: CFE0004601, ucf:49187
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004601

Title: Virtual Motion Camouflage Based Nonlinear Constrained Optimal Trajectory Design Method.
Creator: Basset, Gareth, Xu, Yunjun, Kassab, Alain, Lin, Kuo-Chi, Cho, Hyoung, Qu, Zhihua, University of Central Florida
Abstract / Description: Nonlinear constrained optimal trajectory control is an important and fundamental area of research that continues to advance in numerous fields. Many attempts have been made to present new methods that can solve for optimal trajectories more efficiently or to improve the overall performance of existing techniques. This research presents a recently developed bio-inspired method called the Virtual Motion Camouflage (VMC) method that offers a means of quickly finding, within a defined but varying...
Show moreNonlinear constrained optimal trajectory control is an important and fundamental area of research that continues to advance in numerous fields. Many attempts have been made to present new methods that can solve for optimal trajectories more efficiently or to improve the overall performance of existing techniques. This research presents a recently developed bio-inspired method called the Virtual Motion Camouflage (VMC) method that offers a means of quickly finding, within a defined but varying search space, the optimal trajectory that is equal or close to the optimal solution.The research starts with the polynomial-based VMC method, which works within a search space that is defined by a selected and fixed polynomial type virtual prey motion. Next will be presented a means of improving the solution's optimality by using a sequential based form of VMC, where the search space is adjusted by adjusting the polynomial prey trajectory after a solution is obtained. After the search space is adjusted, an optimization is performed in the new search space to find a solution closer to the global space optimal solution, and further adjustments are made as desired. Finally, a B-spline augmented VMC method is presented, in which a B-spline curve represents the prey motion and will allow the search space to be optimized together with the solution trajectory.It is shown that (1) the polynomial based VMC method will significantly reduce the overall problem dimension, which in practice will significantly reduce the computational cost associated with solving nonlinear constrained optimal trajectory problems; (2) the sequential VMC method will improve the solution optimality by sequentially refining certain parameters, such as the prey motion; and (3) the B-spline augmented VMC method will improve the solution optimality without sacrificing the CPU time much as compared with the polynomial based approach. Several simulation scenarios, including the Breakwell problem, the phantom track problem, the minimum-time mobile robot obstacle avoidance problem, and the Snell's river problem are simulated to demonstrate the capabilities of the various forms of the VMC algorithm. The capabilities of the B-spline augmented VMC method are also shown in a hardware demonstration using a mobile robot obstacle avoidance testbed.
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Date Issued: 2012
Identifier: CFE0004298, ucf:49493
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004298

Title: Bio-Inspired Cooperative Optimal Trajectory Planning for Autonomous Vehicles.
Creator: Remeikas, Charles, Xu, Yunjun, Kassab, Alain, Lin, Kuo-Chi, University of Central Florida
Abstract / Description: With the recent trend for systems to be more and more autonomous, there is a growing need for cooperative trajectory planning. Applications that can be considered as cooperative systems such as surveying, formation flight, and traffic control need a method that can rapidly produce trajectories while considering all of the constraints on the system. Currently most of the existing methods to handle cooperative control are based around either simple dynamics and/or on the assumption that all...
Show moreWith the recent trend for systems to be more and more autonomous, there is a growing need for cooperative trajectory planning. Applications that can be considered as cooperative systems such as surveying, formation flight, and traffic control need a method that can rapidly produce trajectories while considering all of the constraints on the system. Currently most of the existing methods to handle cooperative control are based around either simple dynamics and/or on the assumption that all vehicles have homogeneous properties. In reality, typical autonomous systems will have heterogeneous, nonlinear dynamics while also being subject to extreme constraints on certain state and control variables. In this thesis, a new approach to the cooperative control problem is presented based on the bio-inspired motion strategy known as local pursuit. In this framework, decision making about the group trajectory and formation are handled at a cooperative level while individual trajectory planning is considered in a local sense. An example is presented for a case of an autonomous farming system (e.g. scouting) utilizing nonlinear vehicles to cooperatively accomplish various farming task with minimal energy consumption or minimum time. The decision making and trajectory generation is handled very quickly while being able to consider changing environments laden with obstacles.
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Date Issued: 2013
Identifier: CFE0005053, ucf:49978
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005053

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: Analysis of Behaviors in Crowd Videos.
Creator: Mehran, Ramin, Shah, Mubarak, Sukthankar, Gita, Behal, Aman, Tappen, Marshall, Moore, Brian, University of Central Florida
Abstract / Description: In this dissertation, we address the problem of discovery and representation of group activity of humans and objects in a variety of scenarios, commonly encountered in vision applications. The overarching goal is to devise a discriminative representation of human motion in social settings, which captures a wide variety of human activities observable in video sequences. Such motion emerges from the collective behavior of individuals and their interactions and is a significant source of...
Show moreIn this dissertation, we address the problem of discovery and representation of group activity of humans and objects in a variety of scenarios, commonly encountered in vision applications. The overarching goal is to devise a discriminative representation of human motion in social settings, which captures a wide variety of human activities observable in video sequences. Such motion emerges from the collective behavior of individuals and their interactions and is a significant source of information typically employed for applications such as event detection, behavior recognition, and activity recognition. We present new representations of human group motion for static cameras, and propose algorithms for their application to variety of problems.We first propose a method to model and learn the scene activity of a crowd using Social Force Model for the first time in the computer vision community. We present a method to densely estimate the interaction forces between people in a crowd, observed by a static camera. Latent Dirichlet Allocation (LDA) is used to learn the model of the normal activities over extended periods of time. Randomly selected spatio-temporal volumes of interaction forces are used to learn the model of normal behavior of the scene. The model encodes the latent topics of social interaction forces in the scene for normal behaviors. We classify a short video sequence of $n$ frames as normal or abnormal by using the learnt model. Once a sequence of frames is classified as an abnormal, the regions of anomalies in the abnormal frames are localized using the magnitude of interaction forces.The representation and estimation framework proposed above, however, has a few limitations. This algorithm proposes to use a global estimation of the interaction forces within the crowd. It, therefore, is incapable of identifying different groups of objects based on motion or behavior in the scene. Although the algorithm is capable of learning the normal behavior and detects the abnormality, but it is incapable of capturing the dynamics of different behaviors.To overcome these limitations, we then propose a method based on the Lagrangian framework for fluid dynamics, by introducing a streakline representation of flow. Streaklines are traced in a fluid flow by injecting color material, such as smoke or dye, which is transported with the flow and used for visualization. In the context of computer vision, streaklines may be used in a similar way to transport information about a scene, and they are obtained by repeatedly initializing a fixed grid of particles at each frame, then moving both current and past particles using optical flow. Streaklines are the locus of points that connect particles which originated from the same initial position.This approach is advantageous over the previous representations in two aspects: first, its rich representation captures the dynamics of the crowd and changes in space and time in the scene where the optical flow representation is not enough, and second, this model is capable of discovering groups of similar behavior within a crowd scene by performing motion segmentation. We propose a method to distinguish different group behaviors such as divergent/convergent motion and lanes using this framework. Finally, we introduce flow potentials as a discriminative feature to recognize crowd behaviors in a scene. Results of extensive experiments are presented for multiple real life crowd sequences involving pedestrian and vehicular traffic.The proposed method exploits optical flow as the low level feature and performs integration and clustering to obtain coherent group motion patterns. However, we observe that in crowd video sequences, as well as a variety of other vision applications, the co-occurrence and inter-relation of motion patterns are the main characteristics of group behaviors. In other words, the group behavior of objects is a mixture of individual actions or behaviors in specific geometrical layout and temporal order.We, therefore, propose a new representation for group behaviors of humans using the inter-relation of motion patterns in a scene. The representation is based on bag of visual phrases of spatio-temporal visual words. We present a method to match the high-order spatial layout of visual words that preserve the geometry of the visual words under similarity transformations. To perform the experiments we collected a dataset of group choreography performances from the YouTube website. The dataset currently contains four categories of group dances.
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Date Issued: 2011
Identifier: CFE0004482, ucf:49317
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004482

Title: THE EARLY MODERN SPACE: (CARTOGRAPHIC) LITERATURE AND THE AUTHOR IN PLACE.
Creator: Myers, Michael, Gleyzon, Francois-Xavier, University of Central Florida
Abstract / Description: In geography, maps are a tool of placement which locate both the cartographer and the territory made cartographic. In order to place objects in space, the cartographer inserts his own judgment into the scheme of his design. During the Early Modern period, maps were no longer suspicious icons as they were in the Middle Ages and not yet products of science, but subjects of discourse and works of art. The image of a cartographer's territory depended on his vision�both the nature and placement of...
Show moreIn geography, maps are a tool of placement which locate both the cartographer and the territory made cartographic. In order to place objects in space, the cartographer inserts his own judgment into the scheme of his design. During the Early Modern period, maps were no longer suspicious icons as they were in the Middle Ages and not yet products of science, but subjects of discourse and works of art. The image of a cartographer's territory depended on his vision�both the nature and placement of his gaze�and the product reflected that author's judgment. This is not a study of maps as such but of Early Modern literature, cartographic by nature�the observations of the author were the motif of its design. However, rather than concretize observational judgment through art, the Early Modern literature discussed asserts a reverse relation�the generation of the material which may be observed, the reality, by the views of authors. Spatiality is now an emerging philosophical field of study, taking root in the philosophy of Deleuze & Guattari. Using the notion prevalent in both Postmodern and Early Modern spatiality, which makes of perception a collective delusion with its roots in the critique of Kant, this thesis draws a through-line across time, as texts such as Robert Burton's An Anatomy of Melancholy, Thomas More's Utopia, and selections from William Shakespeare display a tendency to remove value from the standard of representation, to replace meaning with cognition and prioritize a view of views over an observable world. Only John Milton approaches perception as possibly referential to objective reality, by re-inserting his ability to observe and exist in that reality, in a corpus which becomes less generative simulations of material than concrete signposts to his judgment in the world.
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Date Issued: 2015
Identifier: CFH0004899, ucf:53148
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH0004899

Title: IMPROVING AIRLINE SCHEDULE RELIABILITY USING A STRATEGIC MULTI-OBJECTIVE RUNWAY SLOT ASSIGNMENT SEARCH HEURISTIC.
Creator: Hafner, Florian, Sepulveda, Alejandro, University of Central Florida
Abstract / Description: Improving the predictability of airline schedules in the National Airspace System (NAS) has been a constant endeavor, particularly as system delays grow with ever-increasing demand. Airline schedules need to be resistant to perturbations in the system including Ground Delay Programs (GDPs) and inclement weather. The strategic search heuristic proposed in this dissertation significantly improves airline schedule reliability by assigning airport departure and arrival slots to each flight in the...
Show moreImproving the predictability of airline schedules in the National Airspace System (NAS) has been a constant endeavor, particularly as system delays grow with ever-increasing demand. Airline schedules need to be resistant to perturbations in the system including Ground Delay Programs (GDPs) and inclement weather. The strategic search heuristic proposed in this dissertation significantly improves airline schedule reliability by assigning airport departure and arrival slots to each flight in the schedule across a network of airports. This is performed using a multi-objective optimization approach that is primarily based on historical flight and taxi times but also includes certain airline, airport, and FAA priorities. The intent of this algorithm is to produce a more reliable, robust schedule that operates in today's environment as well as tomorrow's 4-Dimensional Trajectory Controlled system as described the FAA's Next Generation ATM system (NextGen). This novel airline schedule optimization approach is implemented using a multi-objective evolutionary algorithm which is capable of incorporating limited airport capacities. The core of the fitness function is an extensive database of historic operating times for flight and ground operations collected over a two year period based on ASDI and BTS data. Empirical distributions based on this data reflect the probability that flights encounter various flight and taxi times. The fitness function also adds the ability to define priorities for certain flights based on aircraft size, flight time, and airline usage. The algorithm is applied to airline schedules for two primary US airports: Chicago O'Hare and Atlanta Hartsfield-Jackson. The effects of this multi-objective schedule optimization are evaluated in a variety of scenarios including periods of high, medium, and low demand. The schedules generated by the optimization algorithm were evaluated using a simple queuing simulation model implemented in AnyLogic. The scenarios were simulated in AnyLogic using two basic setups: (1) using modes of flight and taxi times that reflect highly predictable 4-Dimensional Trajectory Control operations and (2) using full distributions of flight and taxi times reflecting current day operations. The simulation analysis showed significant improvements in reliability as measured by the mean square difference (MSD) of filed versus simulated flight arrival and departure times. Arrivals showed the most consistent improvements of up to 80% in on-time performance (OTP). Departures showed reduced overall improvements, particularly when the optimization was performed without the consideration of airport capacity. The 4-Dimensional Trajectory Control environment more than doubled the on-time performance of departures over the current day, more chaotic scenarios. This research shows that airline schedule reliability can be significantly improved over a network of airports using historical flight and taxi time data. It also provides for a mechanism to prioritize flights based on various airline, airport, and ATC goals. The algorithm is shown to work in today's environment as well as tomorrow's NextGen 4-Dimensional Trajectory Control setup.
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Date Issued: 2008
Identifier: CFE0002067, ucf:47572
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002067

Title: Robust Subspace Estimation Using Low-Rank Optimization. Theory and Applications in Scene Reconstruction, Video Denoising, and Activity Recognition.
Creator: Oreifej, Omar, Shah, Mubarak, Da Vitoria Lobo, Niels, Stanley, Kenneth, Lin, Mingjie, Li, Xin, University of Central Florida
Abstract / Description: In this dissertation, we discuss the problem of robust linear subspace estimation using low-rank optimization and propose three formulations of it. We demonstrate how these formulations can be used to solve fundamental computer vision problems, and provide superior performance in terms of accuracy and running time.Consider a set of observations extracted from images (such as pixel gray values, local features, trajectories...etc). If the assumption that these observations are drawn from a...
Show moreIn this dissertation, we discuss the problem of robust linear subspace estimation using low-rank optimization and propose three formulations of it. We demonstrate how these formulations can be used to solve fundamental computer vision problems, and provide superior performance in terms of accuracy and running time.Consider a set of observations extracted from images (such as pixel gray values, local features, trajectories...etc). If the assumption that these observations are drawn from a liner subspace (or can be linearly approximated) is valid, then the goal is to represent each observation as a linear combination of a compact basis, while maintaining a minimal reconstruction error. One of the earliest, yet most popular, approaches to achieve that is Principal Component Analysis (PCA). However, PCA can only handle Gaussian noise, and thus suffers when the observations are contaminated with gross and sparse outliers. To this end, in this dissertation, we focus on estimating the subspace robustly using low-rank optimization, where the sparse outliers are detected and separated through the `1 norm. The robust estimation has a two-fold advantage: First, the obtained basis better represents the actual subspace because it does not include contributions from the outliers. Second, the detected outliers are often of a specific interest in many applications, as we will show throughout this thesis. We demonstrate four different formulations and applications for low-rank optimization. First, we consider the problem of reconstructing an underwater sequence by removing the turbulence caused by the water waves. The main drawback of most previous attempts to tackle this problem is that they heavily depend on modelling the waves, which in fact is ill-posed since the actual behavior of the waves along with the imaging process are complicated and include several noise components; therefore, their results are not satisfactory. In contrast, we propose a novel approach which outperforms the state-of-the-art. The intuition behind our method is that in a sequence where the water is static, the frames would be linearly correlated. Therefore, in the presence of water waves, we may consider the frames as noisy observations drawn from a the subspace of linearly correlated frames. However, the noise introduced by the water waves is not sparse, and thus cannot directly be detected using low-rank optimization. Therefore, we propose a data-driven two-stage approach, where the first stage (")sparsifies(") the noise, and the second stage detects it. The first stage leverages the temporal mean of the sequence to overcome the structured turbulence of the waves through an iterative registration algorithm. The result of the first stage is a high quality mean and a better structured sequence; however, the sequence still contains unstructured sparse noise. Thus, we employ a second stage at which we extract the sparse errors from the sequence through rank minimization. Our method converges faster, and drastically outperforms state of the art on all testing sequences. Secondly, we consider a closely related situation where an independently moving object is also present in the turbulent video. More precisely, we consider video sequences acquired in a desert battlefields, where atmospheric turbulence is typically present, in addition to independently moving targets. Typical approaches for turbulence mitigation follow averaging or de-warping techniques. Although these methods can reduce the turbulence, they distort the independently moving objects which can often be of great interest. Therefore, we address the problem of simultaneous turbulence mitigation and moving object detection. We propose a novel three-term low-rank matrix decomposition approach in which we decompose the turbulence sequence into three components: the background, the turbulence, and the object. We simplify this extremely difficult problem into a minimization of nuclear norm, Frobenius norm, and L1 norm. Our method is based on two observations: First, the turbulence causes dense and Gaussian noise, and therefore can be captured by Frobenius norm, while the moving objects are sparse and thus can be captured by L1 norm. Second, since the object's motion is linear and intrinsically different than the Gaussian-like turbulence, a Gaussian-based turbulence model can be employed to enforce an additional constraint on the search space of the minimization. We demonstrate the robustness of our approach on challenging sequences which are significantly distorted with atmospheric turbulence and include extremely tiny moving objects. In addition to robustly detecting the subspace of the frames of a sequence, we consider using trajectories as observations in the low-rank optimization framework. In particular, in videos acquired by moving cameras, we track all the pixels in the video and use that to estimate the camera motion subspace. This is particularly useful in activity recognition, which typically requires standard preprocessing steps such as motion compensation, moving object detection, and object tracking. The errors from the motion compensation step propagate to the object detection stage, resulting in miss-detections, which further complicates the tracking stage, resulting in cluttered and incorrect tracks. In contrast, we propose a novel approach which does not follow the standard steps, and accordingly avoids the aforementioned difficulties. Our approach is based on Lagrangian particle trajectories which are a set of dense trajectories obtained by advecting optical flow over time, thus capturing the ensemble motions of a scene. This is done in frames of unaligned video, and no object detection is required. In order to handle the moving camera, we decompose the trajectories into their camera-induced and object-induced components. Having obtained the relevant object motion trajectories, we compute a compact set of chaotic invariant features, which captures the characteristics of the trajectories. Consequently, a SVM is employed to learn and recognize the human actions using the computed motion features. We performed intensive experiments on multiple benchmark datasets, and obtained promising results.Finally, we consider a more challenging problem referred to as complex event recognition, where the activities of interest are complex and unconstrained. This problem typically pose significant challenges because it involves videos of highly variable content, noise, length, frame size ... etc. In this extremely challenging task, high-level features have recently shown a promising direction as in [53, 129], where core low-level events referred to as concepts are annotated and modeled using a portion of the training data, then each event is described using its content of these concepts. However, because of the complex nature of the videos, both the concept models and the corresponding high-level features are significantly noisy. In order to address this problem, we propose a novel low-rank formulation, which combines the precisely annotated videos used to train the concepts, with the rich high-level features. Our approach finds a new representation for each event, which is not only low-rank, but also constrained to adhere to the concept annotation, thus suppressing the noise, and maintaining a consistent occurrence of the concepts in each event. Extensive experiments on large scale real world dataset TRECVID Multimedia Event Detection 2011 and 2012 demonstrate that our approach consistently improves the discriminativity of the high-level features by a significant margin.
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Date Issued: 2013
Identifier: CFE0004732, ucf:49835
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004732

Optimal trajectory design (2)	+ -
Trajectory (2)	+ -
optimal control (2)	+ -
trajectory planning (2)	+ -
4-D trajectories (1)	+ -

ATC preferences (1)	+ -
Activity Recognition (1)	+ -
Airship (1)	+ -
Analysis of manipulators (1)	+ -
Anatomy (1)	+ -
Atmospheric Turbulence (1)	+ -
Augmented Lagrange Multiplier (1)	+ -
Background Subtraction (1)	+ -
Behavior recognition (1)	+ -
Bio-inspired method (1)	+ -
Bio-inspired motion strategies (1)	+ -
CDM (1)	+ -
Camera Motion Estimation (1)	+ -
Cartographic Gaze (1)	+ -
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Chain Form (1)	+ -
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