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A reactive/deliberative planner using genetic algorithms on tactical primitives

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dc.contributor.advisor Christopher Dever and John Deyst. en_US Thrasher, Stephen William en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. en_US 2007-02-20T15:07:36Z 2007-02-20T15:07:36Z 2006 en_US 2006 en_US
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006. en_US
dc.description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. en_US
dc.description Includes bibliographical references (p. 99-102). en_US
dc.description.abstract Unmanned aerial systems are increasingly assisting and replacing humans on so-called dull, dirty, and dangerous missions. In the future such systems will require higher levels of autonomy to effectively use their agile maneuvering capabilities and high-performance weapons and sensors in rapidly evolving, limited-communication combat situations. Most existing vehicle planning methods perform poorly on such realistic scenarios because they do not consider both continuous nonlinear system dynamics and discrete actions and choices. This thesis proposes a flexible framework for forming dynamically realistic, hybrid system plans composed of parametrized tactical primitives using genetic algorithms, which implicitly accommodate hybrid dynamics through a nonlinear fitness function. The framework combines deliberative planning with specially chosen tactical primitives to react to fast changes in the environment, such as pop-up threats. Tactical primitives encapsulate continuous and discrete elements together, using discrete switchings to define the primitive type and both discrete and continuous parameters to capture stylistic variations. This thesis demonstrates the combined reactive/deliberative framework on a problem involving two-dimensional navigation through a field of threats while firing weapons and deploying countermeasures. It also explores the planner's performance with respect to computational resources, problem dimensionality, primitive design, and planner initialization. These explorations can guide further algorithm design and future autonomous tactics research. en_US
dc.description.statementofresponsibility by Stephen William Thrasher, Jr. en_US
dc.format.extent 102 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.subject Aeronautics and Astronautics. en_US
dc.title A reactive/deliberative planner using genetic algorithms on tactical primitives en_US
dc.type Thesis en_US S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. en_US
dc.identifier.oclc 77536235 en_US

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