Multiple autonomous vehicle mission planning and management
Name
48198464-MIT.pdf
Description
Full printable version
Size
11.76 MB
Format
Adobe PDF
Checksum (MD5)
cc1891407d65ec10513e52718c9563da
Author(s)
Zhao, Wei
Advisor(s)
Thomas Magnanti and Stephan Kolitz.
Date Issued
1999
Publisher
Massachusetts Institute of Technology
Abstract
This thesis investigates multiple autonomous vehicle mission planning and management. It begins by introducing the basic concepts and objectives of the multivehicle mission-planning problem. Then it formulates the problem mathematically and analyzes parameters in the objective function. The solution approach uses a hierarchical mission-planning scheme to take advantage of a scalable architecture. We develop a heuristic-based algorithm to solve the multiple-vehicle mission-planning problem. The algorithm has two phases: goal-point partitioning and routing. Goal-point partitioning uses a sweep procedure to group goal-points. Routing uses an implementation of simulated annealing combined with well-known TSP heuristics. Through the computational experiments conducted on both traveling salesman problem test cases, the TSPLIB library, and randomly generated test data, the routing algorithm performs quite well. It has been able to find TSP tours within one percent of optimality, and typically within one-half of one percent. The integration of the two-phase approach provides a solution to the multiple autonomous vehicle mission planning problem.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, System Design & Management Program, 1999.
Includes bibliographical references (leaves 83-85).
Subjects
System Design and Management Program.
MIT Department
System Design and Management Program.
Terms of Use
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