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 17 of 17 for “"Sampling-Based Algorithms"”.
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Sampling-based algorithms for dimension reduction
… number of non-zero entries of A. The proof is based on two sampling techniques called adaptive sampling and volume sampling, and some linear algebraic tools. Low-rank matrix approximation under the Frobenius norm is equivalent to the problem of finding a low-dimensional subspace that minimizes …
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Sampling-based algorithms for stochastic optimal control
… foundations, and provably-correct and efficient sampling-based algorithms to solve stochastic optimal control problems in the presence of complex risk constraints. In the first part of the thesis, we consider the mentioned problems without risk constraints. We propose a novel algorithm called the …
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Incremental sampling based algorithms for state estimation
… of sensor data, it becomes important to have algorithms which can process the available information quickly and provide a timely solution. Also, an inherently continuous world is sensed by robot sensors and converted into discrete packets of information. Algorithms that can take advantage of …
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Sampling-based Algorithms for Fast and Deployable AI
We present sampling-based algorithms with provable guarantees to alleviate the increasingly prohibitive costs of training and deploying modern AI systems. At the core of this thesis lies importance sampling, which we use to construct representative subsets of inputs and compress machine learning …
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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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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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Multi-Robot Path Planning Using Sampling-Based Algorithms and Reinforcement Learning
… and limitations of each. This study puts sampling-based algorithms such as RRT (Rapidly-exploring Random Trees), RRT*, and M* to the test to determine which has the best performance in terms of time spent developing and executing the path plan, overall path plan route length, completion …
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Sampling-based motion planning algorithms for dynamical systems
… proposes efficient approaches for the optimal sampling-based motion planning algorithms, with a strong emphasis on the accommodation of realistic dynamical systems as the subject of motion planning. The main contribution of the dissertation is twofold: advances in general framework for …
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Optimizations for sampling-based motion planning algorithms
Sampling-basedalgorithms solve the motion planning problem by successively solving several separate suproblems of reduced complexity. As a result, the efficiency of the sampling-based algorithm depends on the complexity of each of the algorithms used to solve the individual subproblems, namely the …
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Extensions of motion planning algorithms
Sample-based motion planning algorithms can be applied to a broad range of circumstances in motion planning of robotics. Though sample-based algorithms are able to generate collision-free paths without the information of obstacles, they still have two weaknesses: one is that it is challenging to …
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Reasoning with models of probabilistic knowledge over probabilistic knowledge
… is done within that model. Present model-based approaches of this kind are limited in that either their semantics is restricted to have all agents with a common prior on world states, or are resolving to reasoning algorithms that do not scale to large models. In this thesis we provide the …
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Manipulation with diverse actions
… to a goal configuration. We argue that classic sampling-based techniques cannot solve DAMA problems because of the need to move through lower-dimensional subspaces, and we give two sampling-based algorithms for this problem, DARRT and DARRTCONNECT, based on the RRT and RRTCONNECT algorithms …
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Motion Planning For Autonomous Vehicles In Non-Signalized Intersections
… its performance to the standard RRT and RRT* algorithms through Python simulations. The pRRT algorithm outperformed the RRT and RRT* algorithms in terms of success rate and time to find a safe trajectory. The algorithm was implemented experimentally on scaled cars for the validation of its …
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Planning Practical Paths in High-Dimensional Space
… in the robot's DOF. As a consequence heuristic sampling-based approaches have been developed to solve high-dimensional real-world path planning problems. A shortcoming of the current sampling-based algorithms is that they can obtain highly non-optimal solutions since they rely upon randomization …
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Scalable Structure Learning, Inference, and Analysis with Probabilistic Programs
… probabilistic programs. Third, I present fast algorithms for analyzing statistical properties of probabilistic programs in cases where exact inference is intractable. These algorithms operate entirely through black-box computational interfaces to probabilistic programs and solve challenging …
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Proving infeasibility in motion planning
… high-dimensional motion planning problems is sampling-based algorithms. For motion planning algorithms, completeness is a crucial and desirable attribute. A complete motion planner returns a plan when one exists and also reports failure when no plan exists. However, complete motion planning is …
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Robust sampling-based motion planning for autonomous vehicles in uncertain environments
… under such conditions, motion planning algorithms must be able to rapidly generate smooth, certifiably robust trajectories in real-time. The primary contribution of this thesis is the development of a real-time motion planning framework capable of generating feasible paths for autonomous …