Pregled bibliografske jedinice broj: 691523
Learning swing-free trajectories for UAVs with a suspended load
Learning swing-free trajectories for UAVs with a suspended load // Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2013
Karlsruhe, Njemačka, 2013. str. 4902-4909 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 691523 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Learning swing-free trajectories for UAVs with a suspended load
Autori
Faust, Aleksandra ; Palunko, Ivana ; Cruz, Patricio ; Fierro, Rafael ; Tapia, Lydia
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2013
/ - , 2013, 4902-4909
Skup
IEEE International Conference on Robotics and Automation (ICRA), 2013
Mjesto i datum
Karlsruhe, Njemačka, 06.05.2013. - 10.05.2013
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
autonomous aerial vehicles; helicopters; learning (artificial intelligence); path planning; robot dynamics
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti
