Approximate dynamic programming with applications in multi-agent systems

This thesis presents the development and implementation of approximate dynamic programming methods used to manage multi-agent systems. The purpose of this thesis is to develop an architectural framework and theoretical methods that enable an autonomous mission system to manage real-time multi-agent operations. To meet this goal, we begin by discussing aspects of the real-time multi-agent mission problem. Next, we formulate this problem as a Markov Decision Process (MDP) and present a system architecture designed to improve mission-level functional reliability through system self-awareness and adaptive mission planning. Since most multi-agent mission problems are computationally difficult to solve in real-time, approximation techniques are needed to find policies for these large-scale problems. Thus, we have developed theoretical methods used to find feasible solutions to large-scale optimization problems. More specifically, we investigate methods designed to automatically generate an approximation to the cost-to-go function using basis functions for a given MDP. Next, these these techniques are used by an autonomous mission system to manage multi-agent mission scenarios. Simulation results using these methods are provided for a large-scale mission problem. In addition, this thesis presents the implementation of techniques used to manage autonomous unmanned aerial vehicles (UAVs) performing persistent surveillance operations. We present an indoor multi-vehicle testbed called RAVEN (Real-time indoor Autonomous Vehicle test ENvironment) that was developed to study long-duration missions in a controlled environment.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Valenti, Mario J. (Mario James), 1976-

Advisor dc:contributor.advisor

• Daniela Pucci de Farias and Jonathan P. How.

• Electrical Engineering and Computer Science.

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/1721.1/40330

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/40330