Publication Details
- Keywords:
- SARG
- search and rescue
- team-building
- learning
Abstract
Unmanned aerial vehicles (UAV) are commonly used for search
and rescue missions in unknown environments, where an exact
mathematical model of the environment may not be available.
This paper proposes a framework for the UAV to locate a
missing human after a natural disaster in such environment,
using a reinforcement learning algorithm. We also addressed
the issue with number of states representation and
convergence time for the algorithm by using function
approximation. We conducted both simulated and real
implementations to show how the UAVs can successfully learn
to carry out the task without colliding with obstacles.
Technical aspects for applying reinforcement learning
algorithm to a UAV system and UAV flight control were also
addressed.
Author Details
| Name: | Huy Pham |
| Status: | Inactive |
| Name: | Hung La |
| Status: | Active |
| Name: | David Feil-Seifer |  |
| email: | dave@cse.unr.edu | |
| Website: | http://cse.unr.edu/~dave | |
| Phone: | (775) 784-6469 | |
| Status: | Active |
| Name: | Luan Nguyen |
| Status: | Inactive |
BibTex Reference
title={Reinforcement Learning for Autonomous UAV Navigation Using Function Approximation},
author={Huy X. Pham and Hung La and David Feil-Seifer and Luan Nguyen},
year={2018},
month={August},
address={Philadelphia, PA},
booktitle={IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)},
}
HTML Reference
Support
CHS: Small: Collaborative Research: Spatio-Temporal Situational Awareness in Large-Scale Disasters Using Low-Cost Unmanned Aerial Vehicles, National Science Foundation PI: David Feil-Seifer, Amount: $166,666, Jan. 1, 2016 - Dec. 31, 2017