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Title: AUTONOMOUS ENVIRONMENTAL MAPPING IN MULTI-AGENT UAV SYSTEMS.
Creator: Luotsinen, Linus Jan, Boloni, Ladislau L., University of Central Florida
Abstract / Description: UAV units are by many researchers and aviation specialists considered the future and cutting edge of modern flight technology. This thesis discusses methods for efficient autonomous environmental mapping in a multi-agent domain. An algorithm that emphasizes on team work by sharing the agents local map information and exploration intentions is presented as a solution to the mapping problem. General theories on how to model and implement rational autonomous behaviour for UAV agents are...
Show moreUAV units are by many researchers and aviation specialists considered the future and cutting edge of modern flight technology. This thesis discusses methods for efficient autonomous environmental mapping in a multi-agent domain. An algorithm that emphasizes on team work by sharing the agents local map information and exploration intentions is presented as a solution to the mapping problem. General theories on how to model and implement rational autonomous behaviour for UAV agents are presented. Three different human and tactical behaviour modeling techniques are evaluated. The author found the CxBR paradigm to be the most interesting approach. Also, in order to test and quantify the theories presented in this thesis a simulation environment was developed. This simulation software allows for UAV agents to operate in a visual 3-D environment with mountains, other various terrain types, danger points and enemies to model unexpected events.
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Date Issued: 2004
Identifier: CFE0000051, ucf:46111
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000051

Title: COALITION FORMATION IN MULTI-AGENT UAV SYSTEMS.
Creator: DeJong, Paul, Boloni, Ladislau, University of Central Florida
Abstract / Description: Coalitions are collections of agents that join together to solve a common problem that either cannot be solved individually or can be solved more efficiently as a group. Each individual agent has capabilities that can benefit the group when working together as a coalition. Typically, individual capabilities are joined together in an additive way when forming a coalition. This work will introduce a new operator that is used when combining capabilities, and suggest that the behavior of the...
Show moreCoalitions are collections of agents that join together to solve a common problem that either cannot be solved individually or can be solved more efficiently as a group. Each individual agent has capabilities that can benefit the group when working together as a coalition. Typically, individual capabilities are joined together in an additive way when forming a coalition. This work will introduce a new operator that is used when combining capabilities, and suggest that the behavior of the operator is contextual, depending on the nature of the capability itself. This work considers six different capabilities of Unmanned Air Vehicles (UAV) and determines the nature of the new operator in the context of each capability as coalitions (squadrons) of UAVs are formed. Coalitions are formed using three different search algorithms, both with and without heuristics: Depth-First, Depth-First Iterative Deepening, and Genetic Algorithm (GA). The effectiveness of each algorithm is evaluated. Multi agent-based UAV simulation software was developed and used to test the ideas presented. In addition to coalition formation, the software aims to address additional multi-agent issues such as agent identity, mutability, and communication as applied to UAV systems, in a realistic simulated environment. Social potential fields provide a means of modeling a clustering attractive force at the same time as a collision-avoiding repulsive force, and are used by the simulation to maintain aircraft position relative to other UAVs.
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Date Issued: 2005
Identifier: CFE0000394, ucf:46332
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000394

Title: DYNAMIC TASK ALLOCATION IN MOBILE ROBOT SYSTEMS USING UTILITY FUNTIONS.
Creator: Vander Weide, Scott, Bölöni, Ladislau, University of Central Florida
Abstract / Description: We define a novel algorithm based on utility functions for dynamically allocating tasks to mobile robots in a multi-robot system. The algorithm attempts to maximize the performance of the mobile robot while minimizing inter-robot communications. The algorithm takes into consideration the proximity of the mobile robot to the task, the priority of the task, the capability required by the task, the capabilities of the mobile robot, and the rarity of the capability within the population of mobile...
Show moreWe define a novel algorithm based on utility functions for dynamically allocating tasks to mobile robots in a multi-robot system. The algorithm attempts to maximize the performance of the mobile robot while minimizing inter-robot communications. The algorithm takes into consideration the proximity of the mobile robot to the task, the priority of the task, the capability required by the task, the capabilities of the mobile robot, and the rarity of the capability within the population of mobile robots. We evaluate the proposed algorithm in a simulation study and compare it to alternative approaches, including the contract net protocol, an approach based on the knapsack problem, and random task selection. We find that our algorithm outperforms the alternatives in most metrics measured including percent of tasks complete, distance traveled per completed task, fairness of execution, number of communications, and utility achieved.
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Date Issued: 2008
Identifier: CFE0002274, ucf:47871
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002274

Title: SPATIO-TEMPORAL NEGOTIATION PROTOCOLS.
Creator: Luo, Yi, Boloni, Ladislau, University of Central Florida
Abstract / Description: Canonical problems are simplified representations of a class of real world problems. They allow researchers to compare algorithms in a standard setting which captures the most important challenges of the real world problems being modeled. In this dissertation, we focus on negotiating a collaboration in space and time, a problem with many important real world applications. Although technically a multi-issue negotiation, we show that the problem can not be represented in a satisfactory manner...
Show moreCanonical problems are simplified representations of a class of real world problems. They allow researchers to compare algorithms in a standard setting which captures the most important challenges of the real world problems being modeled. In this dissertation, we focus on negotiating a collaboration in space and time, a problem with many important real world applications. Although technically a multi-issue negotiation, we show that the problem can not be represented in a satisfactory manner by previous models. We propose the "Children in the Rectangular Forest" (CRF) model as a possible canonical problem for negotiating spatio-temporal collaboration. In the CRF problem, two embodied agents are negotiating the synchronization of their movement for a portion of the path from their respective sources to destinations. The negotiation setting is zero initial knowledge and it happens in physical time. As equilibrium strategies are not practically possible, we are interested in strategies with bounded rationality, which achieve good erformance in a wide range of practical negotiation scenarios. We design a number of negotiation protocols to allow agents to exchange their offers. The simple negotiation protocol can be enhanced by schemes in which the agents add additional information of the negotiation flow to aid the negotiation partner in offer formation. Naturally, the performance of a strategy is dependent on the strategy of the opponent and the characteristics of the scenario. Thus we develop a set of metrics for the negotiation scenario which formalizes our intuition of collaborative scenarios (where the agents' interests are closely aligned) versus competitive scenarios (where the gain of the utility for one agent is paid off with a loss of utility for the other agent). Finally, we further investigate the sophisticated strategies which allow agents to learn the opponents while negotiating. We find strategies can be augmented by collaborativeness analysis: the approximate collaborativeness metric can be used to cut short the negotiation. Then, we discover an approach to model the opponent through Bayesian learning. We assume the agents do not disclose their information voluntarily: the learning needs to rely on the study of the offers exchanged during normal negotiation. At last, we explore a setting where the agents are able to perform physical action (movement) while the negotiation is ongoing. We formalize a method to represent and update the beliefs about the valuation function, the current state of negotiation and strategy of the opponent agent using a particle filter. By exploring a number of different negotiation protocols and several peer-to-peer negotiation based strategies, we claim that the CRF problem captures the main challenges of the real world problems while allows us to simplify away some of the computationally demanding but semantically marginal features of real world problems.
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Date Issued: 2011
Identifier: CFE0003722, ucf:48782
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003722

Title: Task Focused Robotic Imitation Learning.
Creator: Abolghasemi, Pooya, Boloni, Ladislau, Sukthankar, Gita, Shah, Mubarak, Willenberg, Bradley, University of Central Florida
Abstract / Description: For many years, successful applications of robotics were the domain of controlled environments, such as industrial assembly lines. Such environments are custom designed for the convenience of the robot and separated from human operators. In recent years, advances in artificial intelligence, in particular, deep learning and computer vision, allowed researchers to successfully demonstrate robots that operate in unstructured environments and directly interact with humans. One of the major...
Show moreFor many years, successful applications of robotics were the domain of controlled environments, such as industrial assembly lines. Such environments are custom designed for the convenience of the robot and separated from human operators. In recent years, advances in artificial intelligence, in particular, deep learning and computer vision, allowed researchers to successfully demonstrate robots that operate in unstructured environments and directly interact with humans. One of the major applications of such robots is in assistive robotics. For instance, a wheelchair mounted robotic arm can help disabled users in the performance of activities of daily living (ADLs) such as feeding and personal grooming. Early systems relied entirely on the control of the human operator, something that is difficult to accomplish by a user with motor and/or cognitive disabilities. In this dissertation, we are describing research results that advance the field of assistive robotics. The overall goal is to improve the ability of the wheelchair / robotic arm assembly to help the user with the performance of the ADLs by requiring only high-level commands from the user. Let us consider an ADL involving the manipulation of an object in the user's home. This task can be naturally decomposed into two components: the movement of the wheelchair in such a way that the manipulator can conveniently grasp the object and the movement of the manipulator itself. This dissertation we provide an approach for addressing the challenge of finding the position appropriate for the required manipulation. We introduce the ease-of-reach score (ERS), a metric that quantifies the preferences for the positioning of the base while taking into consideration the shape and position of obstacles and clutter in the environment. As the brute force computation of ERS is computationally expensive, we propose a machine learning approach to estimate the ERS based on features and characteristics of the obstacles. This dissertation addresses the second component as well, the ability of the robotic arm to manipulate objects. Recent work in end-to-end learning of robotic manipulation had demonstrated that a deep learning-based controller of vision-enabled robotic arms can be thought to manipulate objects from a moderate number of demonstrations. However, the current state of the art systems are limited in robustness to physical and visual disturbances and do not generalize well to new objects. We describe new techniques based on task-focused attention that show significant improvement in the robustness of manipulation and performance in clutter.
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Date Issued: 2019
Identifier: CFE0007771, ucf:52392
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007771

Title: Perceptual Judgment: The Impact of Image Complexity and Training Method on Category Learning.
Creator: Curtis, Michael, Jentsch, Florian, Salas, Eduardo, Szalma, James, Boloni, Ladislau, University of Central Florida
Abstract / Description: The goal of this dissertation was to bridge the gap between perceptual learning theory and training application. Visual perceptual skill has been a vexing topic in training science for decades. In complex task domains, from aviation to medicine, visual perception is critical to task success. Despite this, little, if any, emphasis is dedicated to developing perceptual skills through training. Much of this may be attributed to the perceived inefficiency of perceptual training. Recent applied...
Show moreThe goal of this dissertation was to bridge the gap between perceptual learning theory and training application. Visual perceptual skill has been a vexing topic in training science for decades. In complex task domains, from aviation to medicine, visual perception is critical to task success. Despite this, little, if any, emphasis is dedicated to developing perceptual skills through training. Much of this may be attributed to the perceived inefficiency of perceptual training. Recent applied research in perceptual training with discrimination training, however, holds promise for improved perceptual training efficiency. As with all applied research, it is important to root application in solid theoretical bases. In perceptual learning, the challenge is connecting the basic science to more complex task environments. Using a common aviation task as an applied context, participants were assigned to a perceptual training condition based on variation of image complexity and training type. Following the training, participants were tested for transfer of skill. This was intended to help to ground a potentially useful method of perceptual training in a model category learning, while offering qualitative testing of model fit in increasingly complex visual environments. Two hundred and thirty-one participants completed the computer based training module. Results indicate that predictions of a model of category learning largely extend into more complex training stimuli, suggesting utility of the model in more applied contexts. Although both training method conditions showed improvement across training blocks, the discrimination training condition did not transfer to the post training transfer tasks. Lack of adequate contextual information related to the transfer task in training was attributed to this outcome. Further analysis of the exposure training condition showed that individuals training with simple stimuli performed as well as individuals training on more complex stimuli in a complex transfer task. On the other hand, individuals in the more complex training conditions were less accurate when presented with a simpler representation of the task in transfer. This suggests training benefit to isolating essential task cues from irrelevant information in perceptual judgment tasks. In all, the study provided an informative look at both the theory and application associated with perceptual category learning. Ultimately, this research can help inform future research and training development in domains where perceptual judgment is critical for success.
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Date Issued: 2011
Identifier: CFE0004096, ucf:49139
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004096

Title: Specification and Runtime Checking of Timing Constraints in Safety Critical Java.
Creator: Haddad, Ghaith, Leavens, Gary, Turgut, Damla, Boloni, Ladislau, Nazzal, Dima, University of Central Florida
Abstract / Description: The Java platform is becoming a vital tool for developing real-time and safety-critical systems. Design patterns and the availability of Java libraries, both provide solutions to many known problems. Furthermore, the object-oriented nature of Java simplifies modular development of real-time systems. However, limitations of Java as a programming language for real-time systems are a notable obstacle to producing safe real-time systems. These limitations are found in the unpredictable execution...
Show moreThe Java platform is becoming a vital tool for developing real-time and safety-critical systems. Design patterns and the availability of Java libraries, both provide solutions to many known problems. Furthermore, the object-oriented nature of Java simplifies modular development of real-time systems. However, limitations of Java as a programming language for real-time systems are a notable obstacle to producing safe real-time systems. These limitations are found in the unpredictable execution model of the language, due to Java's garbage collector, and the lack of support for non-functional specification and verification tools. In this dissertation I introduce SafeJML, a specification language for support of functional and non-functional specifications, based on an implementation of a safety-critical Java platform and the Java Modeling Language (JML). This dissertation concentrates on techniques that enable specification and dynamic checking of timing constraints for some important Java features, including methods and subtyping. SafeJML and these dynamic checking techniques allow modular specification and checking of safety-critical systems, including those that use object-orientation and design patterns. Such coding techniques could have maintenance benefits for real-time and safety-critical software.
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Date Issued: 2012
Identifier: CFE0004542, ucf:49224
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004542

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: Machine Learning from Casual Conversation.
Creator: Mohammed Ali, Awrad, Sukthankar, Gita, Wu, Annie, Boloni, Ladislau, University of Central Florida
Abstract / Description: Human social learning is an effective process that has inspired many existing machine learning techniques, such as learning from observation and learning by demonstration. In this dissertation, we introduce another form of social learning, Learning from a Casual Conversation (LCC). LCC is an open-ended machine learning system in which an artificially intelligent agent learns from an extended dialog with a human. Our system enables the agent to incorporate changes into its knowledge base,...
Show moreHuman social learning is an effective process that has inspired many existing machine learning techniques, such as learning from observation and learning by demonstration. In this dissertation, we introduce another form of social learning, Learning from a Casual Conversation (LCC). LCC is an open-ended machine learning system in which an artificially intelligent agent learns from an extended dialog with a human. Our system enables the agent to incorporate changes into its knowledge base, based on the human's conversational text input. This system emulates how humans learn from each other through a dialog. LCC closes the gap in the current research that is focused on teaching specific tasks to computer agents. Furthermore, LCC aims to provide an easy way to enhance the knowledge of the system without requiring the involvement of a programmer. This system does not require the user to enter specific information; instead, the user can chat naturally with the agent. LCC identifies the inputs that contain information relevant to its knowledge base in the learning process. LCC's architecture consists of multiple sub-systems combined to perform the task. Its learning component can add new knowledge to existing information in the knowledge base, confirm existing information, and/or update existing information found to be related to the user input. %The test results indicate that the prototype was successful in learning from a conversation. The LCC system functionality was assessed using different evaluation methods. This includes tests performed by the developer, as well as by 130 human test subjects. Thirty of those test subjects interacted directly with the system and completed a survey of 13 questions/statements that asked the user about his/her experience using LCC. A second group of 100 human test subjects evaluated the dialogue logs of a subset of the first group of human testers. The collected results were all found to be acceptable and within the range of our expectations.
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Date Issued: 2019
Identifier: CFE0007503, ucf:52634
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007503

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: 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: Learning robotic manipulation from user demonstrations.
Creator: Rahmatizadeh, Rouhollah, Boloni, Ladislau, Turgut, Damla, Jha, Sumit Kumar, University of Central Florida
Abstract / Description: Personal robots that help disabled or elderly people in their activities of daily living need to be able to autonomously perform complex manipulation tasks. Traditional approaches to this problem employ task-specific controllers. However, these must to be designed by expert programmers, are focused on a single task, and will perform the task as programmed, not according to the preferences of the user. In this dissertation, we investigate methods that enable an assistive robot to learn to...
Show morePersonal robots that help disabled or elderly people in their activities of daily living need to be able to autonomously perform complex manipulation tasks. Traditional approaches to this problem employ task-specific controllers. However, these must to be designed by expert programmers, are focused on a single task, and will perform the task as programmed, not according to the preferences of the user. In this dissertation, we investigate methods that enable an assistive robot to learn to execute tasks as demonstrated by the user. First, we describe a learning from demonstration (LfD) method that learns assistive tasks that need to be adapted to the position and orientation of the user's head. Then we discuss a recurrent neural network controller that learns to generate movement trajectories for the end-effector of the robot arm to accomplish a task. The input to this controller is the pose of related objects and the current pose of the end-effector itself. Next, we discuss how to extract user preferences from the demonstration using reinforcement learning. Finally, we extend this controller to one that learns to observe images of the environment and generate joint movements for the robot to accomplish a desired task. We discuss several techniques that improve the performance of the controller and reduce the number of required demonstrations. One of this is multi-task learning: learning multiple tasks simultaneously with the same neural network. Another technique is to make the controller output one joint at a time-step, therefore to condition the prediction of each joint on the previous joints. We evaluate these controllers on a set of manipulation tasks and show that they can learn complex tasks, overcome failure, and attempt a task several times until they succeed.
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Date Issued: 2017
Identifier: CFE0006908, ucf:51686
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006908

Title: Quantitative Framework For Social Cultural Interactions.
Creator: Bhatia, Taranjeet, Boloni, Ladislau, Turgut, Damla, Sukthankar, Gita, Fiore, Stephen, University of Central Florida
Abstract / Description: For an autonomous robot or software agent to participate in the social life of humans, it must have a way to perform a calculus of social behavior. Such a calculus must have explanatory power (it must provide a coherent theory for why the humans act the way they do), and predictive power (it must provide some plausible events from the predicted future actions of the humans).This dissertation describes a series of contributions that would allow agents observing or interacting with humans to...
Show moreFor an autonomous robot or software agent to participate in the social life of humans, it must have a way to perform a calculus of social behavior. Such a calculus must have explanatory power (it must provide a coherent theory for why the humans act the way they do), and predictive power (it must provide some plausible events from the predicted future actions of the humans).This dissertation describes a series of contributions that would allow agents observing or interacting with humans to perform a calculus of social behavior taking into account cultural conventions and socially acceptable behavior models. We discuss the formal components of the model: culture-sanctioned social metrics (CSSMs), concrete beliefs (CBs) and action impact functions. Through a detailed case study of a crooked seller who relies on the manipulation of public perception, we show that the model explains how the exploitation of social conventions allows the seller to finalize transactions, despite the fact that the clients know that they are being cheated. In a separate study, we show that how the crooked seller can find an optimal strategy with the use of reinforcement learning.We extend the CSSM model for modeling the propagation of public perception across multiple social interactions. We model the evolution of the public perception both over a single interaction and during a series of interactions over an extended period of time. An important aspect for modeling the public perception is its propagation - how the propagation is affected by the spatio-temporal context of the interaction and how does the short-term and long-term memory of humans affect the overall public perception.We validated the CSSM model through a user study in which participants cognizant with the modeled culture had to evaluate the impact on the social values. The scenarios used in the experiments modeled emotionally charged social situations in a cross-cultural setting and with the presence of a robot. The scenarios model conflicts of cross-cultural communication as well as ethical, social and financial choices. This study allowed us to study whether people sharing the same culture evaluate CSSMs at the same way (the inter-cultural uniformity conjecture). By presenting a wide range of possible metrics, the study also allowed us to determine whether any given metric can be considered a CSSM in a given culture or not.
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Date Issued: 2016
Identifier: CFE0006262, ucf:51047
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006262

Title: Modeling social norms in real-world agent-based simulations.
Creator: Beheshti, Rahmatollah, Sukthankar, Gita, Boloni, Ladislau, Wu, Annie, Swarup, Samarth, University of Central Florida
Abstract / Description: Studying and simulating social systems including human groups and societies can be a complex problem. In order to build a model that simulates humans' actions, it is necessary to consider the major factors that affect human behavior. Norms are one of these factors: social norms are the customary rules that govern behavior in groups and societies. Norms are everywhere around us, from the way people handshake or bow to the clothes they wear. They play a large role in determining our behaviors....
Show moreStudying and simulating social systems including human groups and societies can be a complex problem. In order to build a model that simulates humans' actions, it is necessary to consider the major factors that affect human behavior. Norms are one of these factors: social norms are the customary rules that govern behavior in groups and societies. Norms are everywhere around us, from the way people handshake or bow to the clothes they wear. They play a large role in determining our behaviors. Studies on norms are much older than the age of computer science, since normative studies have been a classic topic in sociology, psychology, philosophy and law. Various theories have been put forth about the functioning of social norms. Although an extensive amount of research on norms has been performed during the recent years, there remains a significant gap between current models and models that can explain real-world normative behaviors. Most of the existing work on norms focuses on abstract applications, and very few realistic normative simulations of human societies can be found. The contributions of this dissertation include the following: 1) a new hybrid technique based on agent-based modeling and Markov Chain Monte Carlo is introduced. This method is used to prepare a smoking case study for applying normative models. 2) This hybrid technique is described using category theory, which is a mathematical theory focusing on relations rather than objects. 3) The relationship between norm emergence in social networks and the theory of tipping points is studied. 4) A new lightweight normative architecture for studying smoking cessation trends is introduced. This architecture is then extended to a more general normative framework that can be used to model real-world normative behaviors. The final normative architecture considers cognitive and social aspects of norm formation in human societies. Normative architectures based on only one of these two aspects exist in the literature, but a normative architecture that effectively includes both of these two is missing.
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Date Issued: 2015
Identifier: CFE0005577, ucf:50244
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005577

Title: Energy efficient routing towards a mobile sink using virtual coordinates in a wireless sensor network.
Creator: Rahmatizadeh, Rouhollah, Boloni, Ladislau, Turgut, Damla, Jha, Sumit, University of Central Florida
Abstract / Description: The existence of a coordinate system can often improve the routing in a wireless sensor network. While most coordinate systems correspond to the geometrical or geographical coordinates, in recent years researchers had proposed the use of virtual coordinates. Virtual coordinates depend only on the topology of the network as defined by the connectivity of the nodes, without requiring geographical information. The work in this thesis extends the use of virtual coordinates to scenarios where the...
Show moreThe existence of a coordinate system can often improve the routing in a wireless sensor network. While most coordinate systems correspond to the geometrical or geographical coordinates, in recent years researchers had proposed the use of virtual coordinates. Virtual coordinates depend only on the topology of the network as defined by the connectivity of the nodes, without requiring geographical information. The work in this thesis extends the use of virtual coordinates to scenarios where the wireless sensor network has a mobile sink. One reason to use a mobile sink is to distribute the energy consumption more evenly among the sensor nodes and thus extend the life-time of the network. We developed two algorithms, MS-DVCR and CU-DVCR which perform routing towards a mobile sink using virtual coordinates. In contrast to the baseline virtual coordinate routing MS-DVCR limits routing updates triggered by the sink movement to a local area around the sink. In contrast, CU-DVCR limits the route updates to a circular area on the boundary of the local area. We describe the design justification and the implementation of these algorithms. Using a set of experimental studies, we show that MS-DVCR and CU-DVCR achieve a lower energy consumption compared to the baseline virtual coordinate routing without any noticeable impact on routing performance. In addition, CU-DVCR provides a lower energy consumption than MS-DVCR for the case of a fast moving sink.
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Date Issued: 2014
Identifier: CFE0005402, ucf:50422
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005402

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: Synthetic generators for simulating social networks.
Creator: Mohammed Ali, Awrad, Sukthankar, Gita, Wu, Annie, Boloni, Ladislau, University of Central Florida
Abstract / Description: An application area of increasing importance is creating agent-based simulations to model human societies. One component of developing these simulations is the ability to generate realistic human social networks. Online social networking websites, such as Facebook, Google+, and Twitter, have increased in popularity in the last decade. Despite the increase in online social networking tools and the importance of studying human behavior in these networks, collecting data directly from these...
Show moreAn application area of increasing importance is creating agent-based simulations to model human societies. One component of developing these simulations is the ability to generate realistic human social networks. Online social networking websites, such as Facebook, Google+, and Twitter, have increased in popularity in the last decade. Despite the increase in online social networking tools and the importance of studying human behavior in these networks, collecting data directly from these networks is not always feasible due to privacy concerns. Previous work in this area has primarily been limited to 1) network generators that aim to duplicate a small subset of the original network's properties and 2) problem-specific generators for applications such as the evaluation of community detection algorithms.In this thesis, we extended two synthetic network generators to enable them to duplicate the properties of a specific dataset. In the first generator, we consider feature similarity and label homophily among individuals when forming links. The second generator is designed to handle multiplex networks that contain different link types. We evaluate the performance of both generators on existing real-world social network datasets, as well as comparing our methods with a related synthetic network generator. In this thesis, we demonstrate that the proposed synthetic network generators are both time efficient and require only limited parameter optimization.
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Date Issued: 2014
Identifier: CFE0005532, ucf:50300
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005532

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: 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: 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

UAV (3)	+ -
Assistive Robotic (2)	+ -
Assistive Robotics (2)	+ -
Autonomous (2)	+ -
Computer Vision (2)	+ -

Learning from Demonstration (2)	+ -
Object Detection (2)	+ -
Robotics (2)	+ -
human-robot interaction (2)	+ -
machine learning (2)	+ -
multi-agent systems (2)	+ -
simulation (2)	+ -
3D Positioning (1)	+ -
Adaboost (1)	+ -
Adaptive (1)	+ -
Adaptive Control (1)	+ -
Advertising (1)	+ -
Affective (1)	+ -
Agent (1)	+ -
Agent system (1)	+ -
Agents (1)	+ -
Argumentation (1)	+ -
Artificial Intelligence (1)	+ -
Artificial Intelligent (1)	+ -
Attention (1)	+ -
Attributes based network (1)	+ -
Baxter Research Robot (1)	+ -
Checking (1)	+ -
Coalition (1)	+ -
Coalition Formation (1)	+ -

University of Central Florida (30)	+ -
Boloni, Ladislau (28)	+ -
Sukthankar, Gita (11)	+ -
Behal, Aman (10)	+ -
Haralambous, Michael (8)	+ -

Turgut, Damla (7)	+ -
Wu, Annie (4)	+ -
Garibay, Ivan (3)	+ -
Xu, Yunjun (3)	+ -
Fiore, Stephen (2)	+ -
Georgiopoulos, Michael (2)	+ -
Laviola II, Joseph (2)	+ -
Lin, Mingjie (2)	+ -
Mohammed Ali, Awrad (2)	+ -
Paperno, Nicholas (2)	+ -
Pourmohammadi Fallah, Yaser (2)	+ -
Rahmatizadeh, Rouhollah (2)	+ -
Shah, Mubarak (2)	+ -
Abolghasemi, Pooya (1)	+ -
Akbas, Mustafa (1)	+ -
Alabachi, Saif (1)	+ -
Bassiouni, Mostafa (1)	+ -
Beheshti, Rahmatollah (1)	+ -
Beidel, Deborah (1)	+ -
Bhatia, Taranjeet (1)	+ -
Boloni, Ladislau L. (1)	+ -
Brawner, Keith (1)	+ -
Brust, Matthias (1)	+ -
Bulumulle, Gamini (1)	+ -
Bölöni, Ladislau (1)	+ -

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