DocumentCode :
3527753
Title :
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
Link To Document :
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