This thesis presents a greedy search algorithm for maneuver-based motion planning of agile vehicles. In maneuver-based motion planning, vehicle maneuvers are solved offline and saved in a library to be used during motion planning. From this library, a tree of possible vehicle states can be generated through the search space. A depth-first, library-based algorithm called AD-Lib is developed and used to quickly provide feasible trajectories along the tree. AD-Lib combines greedy search techniques with hill climbing and effective backtracking to guide the search process rapidly towards the goal. Using simulations of a four-thruster hovercraft, AD-Lib is compared to existing suboptimal search algorithms in both known and unknown environments with static obstacles. AD-Lib is shown to be faster than existing techniques, at the expense of increased path cost. The motion planning strategy of AD-Lib along with a switching controller is also tested in an environment with dynamic obstacles. / Master of Science
Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/36213 |
Date | 29 December 2010 |
Creators | Neas, Charles Bennett |
Contributors | Aerospace and Ocean Engineering, Farhood, Mazen H., Hall, Christopher D., Woolsey, Craig A. |
Publisher | Virginia Tech |
Source Sets | Virginia Tech Theses and Dissertation |
Detected Language | English |
Type | Thesis |
Format | application/pdf |
Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
Relation | Neas_CB_T_2010.pdf |
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