Reinforcement Learning for Autonomous UAV Navigation Using Function Approximation

Ahstract- 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 environ...

Full description

Saved in:
Bibliographic Details
Main Authors: Pham, Huy Xuan, La, Hung Manh, Feil-Seifer, David, Van Nguyen, Luan
Format: Conference Proceeding
Language:English
Subjects:
Approximation algorithms
Function approximation
Laser radar
Learning (artificial intelligence)
NASA
Navigation
Unmanned aerial vehicles
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Ahstract- 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 (RL) algorithm. A function approximation based RL algorithm is proposed to deal with a large number of states representation and to obtain a faster convergence time. 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 RL algorithm to a UAV system and UAV flight control were also addressed.
ISSN:2475-8426
DOI:10.1109/SSRR.2018.8468611