Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 13 of 13 for “"sampling-based methods"”.
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Improving Protein Conformational Sampling by Using Guiding Projections
… we study the conformational space of proteins. Sampling-based motion planning algorithms from the eld of robotics have been very successful at this task. However, studying the conformational space of large proteins with hundreds or thousands of Degrees of Freedom remains a big challenge. In this …
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INFORMED EXPLORATION ALGORITHMS FOR ROBOT MOTION PLANNING AND LEARNING
Sampling-based methods have emerged as a promising technique for solving robot motion-planning problems. These algorithms avoid a priori discretization of the search-space by generating random samples and building a graph online. While the recent advances in this area endow these randomized …
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Planning for Dynamic Nonprehensile Object Transport
Generalized planning methods for dynamic manipulation struggle to efficiently solve kinodynamic constraints. Gradient-based methods suffer from initialization sensitivity, local optimum convergence, and lack of feasibility guarantees, while sampling-based methods can require large computation times …
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Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking
… theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for …
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Risk Aware Planning and Probabilistic Prediction for Autonomous Systems under Uncertain Environments
… of collision with uncertain obstacles. Existing methods to address motion planning problems under uncertainty are either limited to Gaussian uncertainties and convex linear obstacles, or rely on sampling based methods that need uncertainty samples. In this thesis, we consider non-convex uncertain …
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Multi-directional Rapidly Exploring Random Graph (mRRG) for Motion Planning
… to a goal configuration in an environment. Sampling-based path planners are very popular for high-dimensional motion planning in complex environments. These planners build a graph (roadmap) by generating robot configurations (vertices), and connecting nearby pairs of configurations according …
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Efficient solution of the Fokker-Planck Equation via smooth particle hydrodynamics for nonlinear estimation
… of the system. This is especially true for sampling based methods that, while robust and often very simple to implement, quickly become computationally cost ineffective in many applications. Parametric models are usually able to scale far more efficiently as state dimension increases, but …
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Sampling-based algorithms for optimal path planning problems
Sampling-based motion planning received increasing attention during the last decade. In particular, some of the leading paradigms, such the Probabilistic RoadMap (PRM) and the Rapidly-exploring Random Tree (RRT) algorithms, have been demonstrated on several robotic platforms, and found applications …
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Improved Sampling and Variational Inference Methods for Neural Networks
… intractable, resorting to approximate methods is needed. There are two well-established classes of approximate inference applied in practice. First, Markov Chain Monte Carlo (MCMC) constructed to directly sample from the posterior distribution over the network's weights, giving unbiased …
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Robust State Estimation, Uncertainty Quantification, and Uncertainty Reduction with Applications to Wind Estimation
… a computational advantage over Monte Carlo sampling-based methods for uncertainty quantification (UQ) and sensitivity analysis (SA), with time reductions of 38% and 98%, respectively. Lastly, while many estimation approaches achieve desirable accuracy under the assumption of known system …
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Model-based robust and stochastic control, and statistical inference for uncertain dynamical systems
This thesis develops various methods for the robust and stochastic model-based control of uncertain dynamical systems. Several different types of uncertainties are considered, as well as different mathematical formalisms for quantification of the effects of uncertainties in dynamical systems. For …
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Statistical inference for complex networks
… past two decades, a large number of statistical methods have been proposed for modeling such relational data, identifying community structures, hypothesis testing, and model selection. The majority of these methods dealt with the case where only one network observation is available. However, as …
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Graphlet based network analysis
… large networks in recent years. Graphlet based network analysis can vary based on the types of topological structures considered and the kinds of analysis tasks. For example, one of the most popular and early graphlet analyses is based on triples (triangles or paths of length two). …