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A Hybrid Method for Distributed Multi-Agent Mission Planning System

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thesis
posted on 22.04.2020 by Nicholas S Schultz
The goal of this research is to develop a method of control for a team of unmanned aerial and ground robots that is resilient, robust, and scalable given both complete and incomplete information of the environment. The method presented in this paper integrates approximate and optimal methods of path planning integrated with a market-based task allocation strategy. Further work presents a solution to unmanned ground vehicle path planning within the developed mission planning system framework under incomplete information. Deep reinforcement learning is proposed to solve movement through unknown terrain environment. The final demonstration for Advantage-Actor Critic deep reinforcement learning elicits successful implementation of the proposed model.

History

Degree Type

Master of Science in Aeronautics and Astronautics

Department

Aeronautics and Astronautics

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Shaoshuai Mou

Additional Committee Member 2

Dengfeng Sun

Additional Committee Member 3

Inseok Hwang

Licence

Exports