Abstract
Unmanned Aerial Systems (UASs) today are fulfilling more roles than ever before. There is a general push to have these systems feature more advanced autonomous capabilities in the near future. To achieve autonomous behavior it requires some unique approaches for control and decision making. This study used Reinforcement Learning (RL) algorithms to achieve adaptive decision-making capabilities for UASs. An autonomous aerial vehicle was simulated to locate three targets in an unknown area. In order to facilitate the navigation behavior, one navigation agent and one search agent were developed to collaborate in order to meet the requirements of navigating and searching efficiently. Monte Carlo (MC) algorithms and Temporal Difference (TD) algorithms were implemented as the learning methods, their efficiency of navigation and searching were compared. Results were presented and the possible implications to autonomous UAS design were discussed at the end.
| Original language | American English |
|---|---|
| Title of host publication | Proceedings of the 2014 Industrial and Systems Engineering Research Conference |
| State | Published - Jan 2014 |
Keywords
- unmanned aerial systems
- reinforcement learning
- autonomous behavior
- decision making
- Monte Carlo algorithms
- Temporal Difference algorithms
Disciplines
- Aerospace Engineering
- Computer and Systems Architecture
- Digital Communications and Networking