Learning swing-free trajectories for UAVs with a suspended load (CROSBI ID 609463)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Faust, Aleksandra ; Palunko, Ivana ; Cruz, Patricio ; Fierro, Rafael ; Tapia, Lydia
engleski
Learning swing-free trajectories for UAVs with a suspended load
Attaining autonomous flight is an important task in aerial robotics. Often flight trajectories are not only subject to unknown system dynamics, but also to specific task constraints. This paper presents a motion planning method for generating trajectories with minimal residual oscillations (swing-free) for rotorcraft carrying a suspended loads. We rely on a finite-sampling, batch reinforcement learning algorithm to train the system for a particular load. We find criteria that allow the trained agent to be transferred to a variety of models, state and action spaces and produce a number of different trajectories. Through a combination of simulations and experiments, we demonstrate that the inferred policy is robust to noise and the unmodeled dynamics of the system. The contributions of this work are 1) applying reinforcement learning to solve the problem of finding swing-free trajectories for rotorcraft, 2) designing a problem-specific feature vector for value function approximation, 3) giving sufficient conditions for successful learning transfer to different models, state and action spaces, and 4) verification of the resulting trajectories in both simulation and autonomous control of quadrotors with suspended loads.
autonomous aerial vehicles; helicopters; learning (artificial intelligence); path planning; robot dynamics
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Podaci o prilogu
4902-4909.
2013.
objavljeno
Podaci o matičnoj publikaciji
Podaci o skupu
IEEE International Conference on Robotics and Automation (ICRA), 2013
predavanje
06.05.2013-10.05.2013
Karlsruhe, Njemačka