Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
2 results
Search Results
Item Model-Predictive Strategy Generation for Multi-Agent Pursuit-Evasion Games(2015) Raboin, Eric James; Nau, Dana S; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Multi-agent pursuit-evasion games can be used to model a variety of different real world problems including surveillance, search-and-rescue, and defense-related scenarios. However, many pursuit-evasion problems are computationally difficult, which can be problematic for domains with complex geometry or large numbers of agents. To compound matters further, practical applications often require planning methods to operate under high levels of uncertainty or meet strict running-time requirements. These challenges strongly suggest that heuristic methods are needed to address pursuit-evasion problems in the real world. In this dissertation I present heuristic planning techniques for three related problem domains: visibility-based pursuit-evasion, target following with differential motion constraints, and distributed asset guarding with unmanned sea-surface vehicles. For these domains, I demonstrate that heuristic techniques based on problem relaxation and model-predictive simulation can be used to efficiently perform low-level control action selection, motion goal selection, and high-level task allocation. In particular, I introduce a polynomial-time algorithm for control action selection in visibility-based pursuit-evasion games, where a team of pursuers must minimize uncertainty about the location of an evader. The algorithm uses problem relaxation to estimate future states of the game. I also show how to incorporate a probabilistic opponent model learned from interaction traces of prior games into the algorithm. I verify experimentally that by performing Monte Carlo sampling over the learned model to estimate the location of the evader, the algorithm performs better than existing planning approaches based on worst-case analysis. Next, I introduce an algorithm for motion goal selection in pursuit-evasion scenarios with unmanned boats. I show how a probabilistic model accounting for differential motion constraints can be used to project the future positions of the target boat. Motion goals for the pursuer boat can then be selected based on those projections. I verify experimentally that motion goals selected with this technique are better optimized for travel time and proximity to the target boat when compared to motion goals selected based on the current position of the target boat. Finally, I introduce a task-allocation technique for a team of unmanned sea-surface vehicles (USVs) responsible for guarding a high-valued asset. The team of USVs must intercept and block a set of hostile intruder boats before they reach the asset. The algorithm uses model-predictive simulation to estimate the value of high-level task assignments, which are then realized by a set of learned low-level behaviors. I show experimentally that using model-predictive simulations based on Monte-Carlo sampling is more effective than hand-coded evaluation heuristics.Item DESIGN, DEVELOPMENT, AND EVALUATION OF A DISCRETELY ACTUATED STEERABLE CANNULA(2014) Ayvali, Elif; Desai, Jaydev P; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Needle-based procedures require the guidance of the needle to a target region to deliver therapy or to remove tissue samples for diagnosis. During needle insertion, needle deflection occurs due to needle-tissue interaction which deviates the needle from its insertion direction. Manipulating the needle at the base provides limited control over the needle trajectory after the insertion. Furthermore, some sites are inaccessible using straight-line trajectories due to delicate structures that need to be avoided. The goal of this research is to develop a discretely actuated steerable cannula to enable active trajectory corrections and achieve accurate targeting in needle-based procedures. The cannula is composed of straight segments connected by shape memory alloy (SMA) actuators and has multiple degrees-of-freedom. To control the motion of the cannula two approaches have been explored. One approach is to measure the cannula configuration directly from the imaging modality and to use this information as a feedback to control the joint motion. The second approach is a model-based controller where the strain of the SMA actuator is controlled by controlling the temperature of the SMA actuator. The constitutive model relates the stress, strain and the temperature of the SMA actuator. The uniaxial constitutive model of the SMA that describes the tensile behavior was extended to one-dimensional pure- bending case to model the phase transformation of the arc-shaped SMA wire. An experimental characterization procedure was devised to obtain the parameters of the SMA that are used in the constitutive model. Experimental results demonstrate that temperature feedback can be effectively used to control the strain of the SMA actuator and image feedback can be reliably used to control the joint motion. Using tools from differential geometry and the configuration control approach, motion planning algorithms were developed to create pre-operative plans that steer the cannula to a desired surgical site (nodule or suspicious tissue). Ultrasound-based tracking algorithms were developed to automate the needle insertion procedure using 2D ultrasound guidance. The effectiveness of the proposed in-plane and out-of-plane tracking methods were demonstrated through experiments inside tissue phantom made of gelatin and ex-vivo experiments. An optical coherence tomography probe was integrated into the cannula and in-situ microscale imaging was performed. The results demonstrate the use of the cannula as a delivery mechanism for diagnostic applications. The tools that were developed in this dissertation form the foundations of developing a complete steerable-cannula system. It is anticipated that the cannula could be used as a delivery mechanism in image-guided needle-based interventions to introduce therapeutic and diagnostic tools to a target region.