2026-09-19T08:30:36Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/352922022-01-13T07:54:11Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Marc McConley and Brent Appleby.
Krenzke, Tom (Tom Paul)
Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2007-01-10T15:35:17Z
2007-01-10T15:35:17Z
2006
2006
http://hdl.handle.net/1721.1/35292
74278842
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 67-69).
With the need for greater autonomy in unmanned vehicles growing, design of algorithms for mission-level planning becomes essential. The general field of motion planning for unmanned vehicles falls into this category. Of particular interest is the case of operating in hostile environments with unknown threat locations. When a threat appears, a replan must be quickly formulated and executed. The use of terrain masking to hide from the threat is a vital tactic, which a good algorithm should exploit. In addition, the algorithm should be able to accommodate large search spaces and non-linear objective functions. This thesis investigates the suitability of the Ant Colony Optimization (ACO) heuristic for the agile vehicle motion planning problem. An ACO implementation tailored to the motion planning problem was designed and tested against an existing genetic algorithm solution method for validation. Results show that ACO is indeed a viable option for real-time trajectory generation. ACO' ability to incorporate heuristic information, and its method of solution construction, make it better suited to motion planning problems than existing methods.
by Tom Krenzke.
S.M.
69 p.
1912374 bytes
1975792 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Aeronautics and Astronautics.
Ant colony optimization for agile motion planning
Thesis
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<Abstract>With the need for greater autonomy in unmanned vehicles growing, design of algorithms for mission-level planning becomes essential. The general field of motion planning for unmanned vehicles falls into this category. Of particular interest is the case of operating in hostile environments with unknown threat locations. When a threat appears, a replan must be quickly formulated and executed. The use of terrain masking to hide from the threat is a vital tactic, which a good algorithm should exploit. In addition, the algorithm should be able to accommodate large search spaces and non-linear objective functions. This thesis investigates the suitability of the Ant Colony Optimization (ACO) heuristic for the agile vehicle motion planning problem. An ACO implementation tailored to the motion planning problem was designed and tested against an existing genetic algorithm solution method for validation. Results show that ACO is indeed a viable option for real-time trajectory generation. ACO' ability to incorporate heuristic information, and its method of solution construction, make it better suited to motion planning problems than existing methods.</Abstract>
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