Browsing by Subject "trajectory optimization"
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Item Multi-layer approach to motion planning in obstacle rich environment(2009-05-15) Kim, Sung HyunA widespread use of robotic technology in civilian and military applications has generated a need for advanced motion planning algorithms that are real-time implementable. These algorithms are required to navigate autonomous vehicles through obstacle-rich environments. This research has led to the development of the multilayer trajectory generation approach. It is built on the principle of separation of concerns, which partitions a given problem into multiple independent layers, and addresses complexity that is inherent at each level. We partition the motion planning algorithm into a roadmap layer and an optimal control layer. At the roadmap layer, elements of computational geometry are used to process the obstacle rich environment and generate feasible sets. These are used by the optimal control layer to generate trajectories while satisfying dynamics of the vehicle. The roadmap layer ignores the dynamics of the system, and the optimal control layer ignores the complexity of the environment, thus achieving a separation of concern. This decomposition enables computationally tractable methods to be developed for addressing motion planning in complex environments. The approach is applied in known and unknown environments. The methodology developed in this thesis has been successfully applied to a 6 DOF planar robotic testbed. Simulation results suggest that the planner can generate trajectories that navigate through obstacles while satisfying dynamical constraints.Item New methods for estimation, modeling and validation of dynamical systems using automatic differentiation(Texas A&M University, 2005-02-17) Griffith, Daniel ToddThe main objective of this work is to demonstrate some new computational methods for estimation, optimization and modeling of dynamical systems that use automatic differentiation. Particular focus will be upon dynamical systems arising in Aerospace Engineering. Automatic differentiation is a recursive computational algorithm, which enables computation of analytically rigorous partial derivatives of any user-specified function. All associated computations occur, in the background without user intervention, as the name implies. The computational methods of this dissertation are enabled by a new automatic differentiation tool, OCEA (Object oriented Coordinate Embedding Method). OCEA has been recently developed and makes possible efficient computation and evaluation of partial derivatives with minimal user coding. The key results in this dissertation details the use of OCEA through a number of computational studies in estimation and dynamical modeling. Several prototype problems are studied in order to evaluate judicious ways to use OCEA. Additionally, new solution methods are introduced in order to ascertain the extended capability of this new computational tool. Computational tradeoffs are studied in detail by looking at a number of different applications in the areas of estimation, dynamical system modeling, and validation of solution accuracy for complex dynamical systems. The results of these computational studies provide new insights and indicate the future potential of OCEA in its further development.