Multiple autonomous vehicle mission planning and management

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.

Department dc:contributor.department

System Design and Management Program.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

1999

Author dc:creator

• Zhao, Wei

Advisor dc:contributor.advisor

• Thomas Magnanti and Stephan Kolitz.

• System Design and Management Program.

Statement 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/29165

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/29165