A reinforcement learning approach towards autonomous suspended load manipulation using aerial robots

Author

Palunko, Ivana ; Faust, Aleksandra ; Cruz, Pedro ; Tapia, Lydia ; Fierro, Rafael

Author_Institution

Fac. of Electr. Eng. & Comput., Univ. of Zagreb, Zagreb, Croatia

fYear

2013

fDate

6-10 May 2013

Firstpage

4896

Lastpage

4901

Abstract

In this paper, we present a problem where a suspended load, carried by a rotorcraft aerial robot, performs trajectory tracking. We want to accomplish this by specifying the reference trajectory for the suspended load only. The aerial robot needs to discover/learn its own trajectory which ensures that the suspended load tracks the reference trajectory. As a solution, we propose a method based on least-square policy iteration (LSPI) which is a type of reinforcement learning algorithm. The proposed method is verified through simulation and experiments.

Keywords

autonomous aerial vehicles; helicopters; iterative methods; learning (artificial intelligence); least squares approximations; trajectory control; autonomous suspended load manipulation; least-square policy iteration; reference trajectory tracking; reinforcement learning algorithm; rotorcraft aerial robot; suspended load; Computational modeling; Legged locomotion; Target tracking; Aerial robotics; aerial load transportation; machine learning; motion planning and control; quadrotor control; reinforcement learning; trajectory tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation (ICRA), 2013 IEEE International Conference on

Conference_Location

Karlsruhe

ISSN

1050-4729

Print_ISBN

978-1-4673-5641-1

Type

conf

DOI

10.1109/ICRA.2013.6631276

Filename

6631276