A reinforcement learning approach towards autonomous suspended load manipulation using aerial robots (CROSBI ID 609464)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Palunko, Ivana ; Faust, Aleksandra ; Cruz, Patricio ; Tapia, Lydia ; Fierro, Rafael
engleski
A reinforcement learning approach towards autonomous suspended load manipulation using aerial robots
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.
autonomous aerial vehicles; helicopters; learning (artificial intelligence); path planning; robot dynamics
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Podaci o prilogu
4896-4901.
2013.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2013
Institute of Electrical and Electronics Engineers (IEEE)
Podaci o skupu
IEEE International Conference on Robotics and Automation (ICRA), 2013
predavanje
06.05.2013-10.05.2013
Karlsruhe, Njemačka