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Current Hiroaki Kawashima (Visiting Scholar) Peter Kingston (Ph.D. student) Philip Twu (Ph.D. student) Rahul Chipalkatty (Ph.D. student) Amy LaViers (Ph.D. student) Greg Droge (Ph.D. student) Hassan Jaleel (Ph.D. student) Waseem Abbas (Ph.D. student) Jean-Pierre de la Croix (Ph.D. student) Smriti Chopra (Ph.D. student) Rowland O'Flaherty (M.S. student) Thiagarajan Ramachandran (M.S. student) Eugene Gargas (M.S. student) Zak Costello (M.S. student) |
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Former Ph.D. Students
Jonghoek Kim (Ph.D. Spring 2011)
Thesis: Simultaneous Cooperative Exploration and Networking
Abstract: This thesis provides strategies for multiple vehicles to explore unknown environments in a cooperative and systematic manner. These strategies are called Simultaneous Cooperative Exploration and Networking (SCENT) strategies. As the basis for development of SCENT strategies, we first tackle the motion control and planning for one vehicle with range sensors. In particular, we develop the curve-tracking controllers for autonomous vehicles with rigidly mounted range sensors, and a provably complete exploration strategy is proposed so that one vehicle with range sensors builds a topological map of an environment. The SCENT algorithms introduced in this thesis extend the exploration strategy for one vehicle to multiple vehicles. The enabling idea of the SCENT algorithms is to construct a topological map of the environment, which is considered completely explored if the map corresponds to a complete Voronoi diagram of the environment. To achieve this, each vehicle explores its local area by incrementally expanding the already visited areas of the environment. At the same time, every vehicle deploys communication devices at selected locations and, as a result, a communication network is created concurrently with a topological map. This additional network allows the vehicles to share information in a distributed manner resulting in an efficient exploration of the workspace.
Musad Haque (Ph.D. Fall 2010)
Thesis: Biologically Inspired Heterogeneous Multi-Agent Systems
Abstract: Many biological systems are known to accomplish complex tasks in a decentralized, robust, and scalable manner - characteristics that are desirable to the coordination of engineered systems as well. Inspired by nature, we produce coordination strategies for a network of heterogenous agents and in particular, we focus on intelligent collective systems. Bottlenose dolphins and African lions are examples of intelligent collective systems since they exhibit sophisticated social behaviors and effortlessly transition between functionalities. Through preferred associations, specialized roles, and self-organization, these systems forage prey, form alliances, and maintain sustainable group sizes. In this thesis, we take a three-phased approach to bioinspiration: in the first phase, we produce agent-based models of specific social behaviors observed in nature. The goal of these models is to capture the underlying biological phenomenon, yet remain simple so that the models are amenable to analysis. In the second phase, we produce bio-inspired algorithms which are based on the simple biological models produced in the first phase. Moreover, these algorithms are developed in the context of specific coordination tasks, e.g., the multi-agent foraging task. In the final phase of this work, we tailor these algorithms to produce coordination strategies that are ready to be deployed in target applications.
Patrick Martin (Ph.D. Spring 2010)
Thesis: Motion Description Languages: From Specification to Execution
Abstract: Many emerging controls applications have seen increased operational complex- ity due to the deployment of embedded, networked systems that must interact with the physical environment. In order to manage this complexity, we design different control modes for each system and use motion description languages (MDL) to specify a sequence of these controllers to execute at run-time. Unfortunately, current MDL frameworks lose some of the important details (i.e. power, spatial, or communication capabilities) that affect the execution of the control modes. This work presents sev- eral computational tools that work towards closing MDL˘s specification-to-execution gap, which can result in undesirable behavior of complex systems at run-time. First, we develop the notion of an MDL compiler for control specifications with spatial, energy, and temporal constraints. We define a new MDL for networked systems and develop an algorithm that automatically generates a supervisor to prevent incorrect execution of the multi-agent MDL program. Additionally, we derive conditions for checking if an MDL program satisfies actuator constraints and develop an algorithm to insert new control modes that maintain actuator bounds during the execution of the MDL program. Finally, we design and implement a software architecture that facilitates the development of control applications for systems with power, actuator, sensing, and communication constraints.
Xu Chu (Dennis) Ding (Ph.D. Fall 2009)
Thesis: Real-Time Optimal Control of Autonomous Switched Systems
Abstract: In this work, we provide a real-time algorithmic optimal control framework for autonomous switched systems. Traditional optimal control approaches for autonomous switched system are open-loop in nature. Therefore, the switching times of the system can not be adjusted or adapted when the system parameters or the operational environments change. We aim to close this loop, and apply adaptations to the optimal switching strategy based on new information that can only be captured on-line. One important contribution of this work is to provide the means to allow feedback (in a general sense) to the control laws (i.e. the switching times) of the switched system so that the control law can be updated to maintain optimality of the switching-time control inputs. Furthermore, convergence analyses for the proposed algorithms are presented. Finally, we apply the real-time algorithms to an application in optimal formation and coverage control of a networked system. This application is implemented on a realistic simulation framework consisting of a number of Unmanned Aerial Vehicles (UAVs) that interact in a virtual 3D world.
Brian Smith (Ph.D. Spring 2009)
Thesis: Automatic Coordination and Deployment of Multi-Robot Systems
Abstract: We present automatic tools for configuring and deploying multi-robot networks of decentralized, mobile robots. These methods are tailored to the decentralized nature of the multi-robot network and the limited information available to each robot. We present methods for determining if user-defined network tasks are feasible or infeasible for the network, considering the limited range of its sensors. To this end, we define rigid and persistent feasibility and present necessary and sufficient conditions (along with corresponding algorithms) for determining the feasibility of arbitrary, user-defined deployments. Control laws for moving multi-robot networks in acyclic, persistent formations are defined. We also present novel Embedded Graph Grammar Systems (EGGs) for coordinating and deploying the network. These methods exploit graph representations of the network, as well as graph-based rules that dictate how robots coordinate their control. Automatic systems are defined that allow the robots to assemble arbitrary, user-defined formations without any reliance on localization. Further, this system is augmented to deploy these formations at the user-defined, global location in the environment, despite limited localization of the network. The culmination of this research is an intuitive software program with a Graphical User Interface (GUI) and a satellite image map which allows users to enter the desired locations of sensors. The automatic tools presented here automatically configure an actual multi-robot network to deploy and execute user-defined network tasks.
Meng Ji (Ph.D. Summer 2007)
Thesis: Graph-Based Control of Networked Systems
Abstract: Networked systems have attracted great interests from the control society during the last decade. Several issues rising from the recent research are addressed in this dissertation. Connectedness is one of the important conditions that enable distributed coordination in a networked system. Nonetheless, it has been assumed in most implementations, especially in continuous-time applications, until recently. A nonlinear weighting strategy is proposed in this dissertation to solve the connectedness preserving problem. Both rendezvous and formation problem are addressed in the context of homogeneous network. Controllability of heterogeneous networks is another issue which has been long omitted. This dissertation contributes a graph theoretical interpretation of controllability. Distributed sensor networks make up another important class of networked systems. A novel estimation strategy is proposed in this dissertation. The observability problem is raised in the context of our proposed distributed estimation strategy, and a graph theoretical interpretation is derived as well.
Tejas Mehta (Ph.D. Spring 2007)
Thesis: Optimal, Multi-Modal Control With Applications to Robotics
Abstract: Multi-modal control is a commonly used design tool to deal with increasing complexity associated with modern control tasks. The main idea in multi-modal control is to breakup complex control tasks into simpler tasks. In particular, number of control modes are constructed, each with respect to a particular task, and these modes are combined according to some supervisory control logic in order to complete the overall control task. This way of modularizing the control task lends itself particularly well to the control of autonomous mobile robot, as evidenced by the success of behavior-based robotics. Many challenging and interesting research issues arise when employing multi-modal control. This dissertation aims to address these issues within an optimal control framework and apply the resulting theory to develop effective navigation strategies for autonomous mobile robots. To this end, we first addressed the problem of inferring global behaviors from a collection of local rules (i.e., feedback control laws). Next, we addressed the issue of adaptively varying the multi-modal control system to further improve performance. Inspired by adaptive multi-modal control, we presented a constructivist framework for the learning from example problem. Next, we addressed the optimal control of multi-modal systems with infinite dimensional constraints. Finally, we used multi-modal control to develop effective navigation strategies for autonomous mobile robots. In closing, the main strength of multi-modal control lies in breaking up complex control task into simpler tasks. This divide-and-conquer approach helps modularize the control system. This has the same effect on complex control systems that object-oriented programming has for large-scale computer programs, namely it allows greater simplicity, flexibility, and adaptability.
David Wooden (Ph.D. Fall 2006)
Thesis: Graph-based Path Planning for Mobile Robots
Abstract: Mobile robots are increasingly moving from structured spaces (e.g. office buildings) to unstructured space (e.g. unfamiliar outdoor terrain), from plodding along (1 mph) to high speed (70 mph), and from accurate, active sensing (LIDAR, IR) to passive noisy sensing (vision). This thesis presents work on how to solve planning and navigation control problems for mobile robots in unknown unstructured environments in the presence of noisy perception. The centerpiece of this work is a sparse, combinatorial approach to path planning that is dynamic and fast. Additional work is presented that augments a standard control architecture to include a feedback mechanism that handles inconsistencies in configuration spaces. Also, a method for quickly finding globally optimal paths in a colored graph is presented, given our sense of edge coloring and optimality. All the presented methods were implemented on various real-world mobile platforms, such as the LAGR robot, iRobot Magellans, and Georgia Tech's entry to the Urban Grand Challenge; the work is presented with attention given to both theoretical and practical considerations.
Florent Delmotte (Ph.D. Fall 2006)
Thesis: Multi-Modal Control: From Motion Description Languages to Optimal Control
Abstract: The goal of the proposed research is to provide efficient methods for defining, selecting and encoding multi-modal control programs. To this end, modes are recovered from system observations, i.e. quantized input-output strings are converted into consistent mode sequences within the Motion Description Language (MDL) framework. The design of such modes can help identify and predict the behaviors of complex systems (e.g. biological systems such as insects) and inspire the design and control of robust semi-autonomous systems (e.g. navigating robots). In this work, the efficiency of a method will be defined by the complexity and expressiveness of specific control programs. The insistence on low-complexity programs is originally motivated by communication constraints on the computer control of semi-autonomous systems, but also by our belief that, as complex as they may look, natural systems indeed use short motion schemes with few basic behaviors. The attention is first focused on the design of such short-length, few-distinct-modes mode sequences within the MDL framework. Optimal control problems are then addressed. In particular, given a mode sequence, the question of deciding when the system should switch from one mode to another in order to achieve some reachability requirements is studied. Finally, we propose to investigate how sampling strategies affect complexity and reachability, and how an acceptable trade-off between these conflicting entities can be reached.
Henrik Axelsson (Ph.D. Spring 2006)
Thesis: Optimal Control of Switched Autonomous Systems: Theory, Algorithms, and Robotic Applications
Abstract: As control systems are becoming more and more complex, system complexity is rapidly becoming a limiting factor in the efficacy of established techniques for control systems design. To cope with the growing complexity, control architectures often have a hierarchical structure. At the base of the system pyramid lie feedback loops with simple closed-loop control laws. These are followed, at a higher level, by discrete control logics. Such hierarchical systems typically have a hybrid nature. A common approach to addressing these types of complexity consists of decomposing, in the time domain, the control task into a number of modes, i.e. control laws dedicated to carrying out a limited task. This type of control generally involves switching laws among the various modes, and its design poses a major challenge in many application domains. The primary goal of this thesis is to develop a unified framework for addressing this challenge. To this end, the contribution of this thesis is threefold: 1. An algorithmic framework for how to optimize the performance of switched autonomous systems is derived. The optimization concerns both the sequence in which different modes appear in and the duration of each mode. The optimization algorithms are presented together with detailed convergence analyses. 2. Control strategies for how to optimize switched autonomous systems operating in real time, and when the initial state of the system is unknown, are presented. 3. A control strategy for how to optimally navigate an autonomous mobile robot in real-time is presented and evaluated on a mobile robotics platform. The control strategy uses optimal switching surfaces for when to switch between different modes of operations (behaviors).
Abubakr Muhammad (Ph.D. Fall 2005)
Thesis: Graphs, Simplicial Complexes and Beyond: Topological Tools for Multi-agent Coordination
Abstract: In this work, connectivity graphs have been studied as models of local interactions in multi-agent robotic systems. A systematic study of the space of connectivity graphs has been done from a geometric and topological point of view. Some results on the realization of connectivity graphs in their respective configuration spaces have been given. A complexity analysis of networks, from the point of view of intrinsic structural complexity, has been given. Various topological spaces in networks, as induced from their connectivity graphs, have been recognized and put into applications, such as those concerning coverage problems in sensor networks. A framework for studying dynamic connectivity graphs has been proposed. This framework has been used for several applications that include the generation of low-complexity formations as well as collaborative beamforming in sensor networks. The theory has been verified by generating extensive simulations, with the help of software tools of computational homology and semi-definite programming. Finally, several open problems and areas of further research have been identified.
Mohamed Babaali (Ph.D. Spring 2004)
Thesis: Switched Linear Systems: Observability and Observers
Abstract: Switched linear systems have long been subject to high interest and intense research efforts, not only because many real world systems happen to exhibit switching behaviors, but also because the control of many complex systems is only possible via the combination of classical continuous control laws with supervisory switching logic. A particularly important problem is that of estimator and observer design, since the state of a system is usually only available through partial, often noise-corrupted, measurements. Even though hybrid estimation has been around for at least thirty years, a veil of mystery has surrounded the concept of ``observability' in switched linear systems. It is not until recently, with the recent renewal of interest toward deterministic hybrid systems, that observer design and observability analysis have fuelled sustained research efforts. It is in this context that this work is grounded. More precisely, the objective of this research is twofold: - To define proper concepts of observability in discrete-time switched linear systems, to characterize them, and to analyze their main properties, among which decidability is of special importance. - To propose and analyze observers - deadbeat and asymptotic - for such systems. The main contributions of this dissertation are as follows. It is shown that pathwise observability, i.e. state observability under arbitrary mode sequences, is decidable. Furthermore, the Kalman-Bertram sampling criterion is carried over to switched linear systems. Under unknown modes, mode and state observability are both characterized through simple linear algebraic tests, and are shown to be decidable in the autonomous case. As for asymptotic observers, a direct algebraic approach is proposed for the class of linear systems subjected to switching in the measurement equation.
Leandro Barajas (Ph.D. Spring 2003)
Thesis: Process Control in High-noise Environments Using a Limited Number of Measurements
Abstract: The topic of this dissertation is the derviation, development, and evaluation of novel hybrid algorithms for process control that use a limited number of measurements and that are suitable to operate in the presence of large amounts of process noise. As an initial step, affine and neural network statistical process models are developed in order to simulate the steady-state system behavior. Such models are vitally important in the evaluation, testing, and improvement of all other process controllers referred to in this work. Afterwards, fuzzy logic controller rules are assimilated into a mathematical characterization of a model that includes the modes and mode transition rules that define a hybrid hierarchical process controller. The main processing entity in such a framework is a closed-loop control algorithm that performas global and then local optimizations in order to asymptotically reach minimum bias error; this is done while requiring a minimum number of iterations in order to promptly reach a desired operational windwow. The results of this research are applied to surface mount technology manufacturing-lines yield optimization. This work achieves a practical degree of control over the solder-paste volume deposition in the Stencil Printing Process (SPP). Results show that it is possible to change the operating point of the process by modifying certain machine parameters and even compensate for the difference in height due to change in print direction.
Former M.S. Students
Daniel Pickem (M.S. Fall 2011)
Akash Verma (M.S. Fall 2011)
Edward MacDonald (M.S. Summer 2011)
Amjad Dawd (M.S. Spring 2010)
Akhil Bahl (M.S. Fall 2009)
Daniel Sinto (M.S. Spring 2009)
Jiuguang Wang (M.S. Spring 2009)
Angela Schoelling (M.S. Summer 2007)
Johan Isaksson (M.S. Spring 2006)
Lucas Osorio (M.S. Spring 2005)
Adam Austin (M.S. Spring 2003)
Other Former Group Members
Amir Rahmani (Post-Doc 2008-2011, Now Asst. Prof. at Univ. Miami)
Mauro Franceschelli (Visitor from Univ. Calgiari, Italy, 2009)
Rosalba Galvan-Guerra (Visitor from CINVESTAV, Mexico City, 2009)
Axel Schild (Visitor from Univ. Bochum, Germany, 2009)
Simone Martini (Visitor from Pisa, Italy, 2008, 2009)
Staffan Bjorkenstam (Visitor from Chalmers, Sweden, 2006)
Shun-ichi Azuma (Post Doc 2004. Currently Assistant Prof. at Kyoto University, Japan)
Anders Gustafsson (Visitor from KTH, Stockholm, Sweden, 2003.)
Mauro Boccadoro (Visitor from Universita di Perugia, Italy, 2004.)