TY - GEN
T1 - Flying through gates using a behavioral cloning approach
AU - Rodriguez-Hernandez, Erick
AU - Vasquez-Gomez, Juan Irving
AU - Herrera-Lozada, Juan Carlos
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Drone racing presents a challenge to autonomous micro aerial vehicles (MAV) because usually the track is not known in advance and it is affected by the environment light. In such scenarios, the vehicle has to act quickly depending on the information provided by its sensors. In this work, we want to predict the movement of the drone so that it passes through a gate. Unlike previous approaches where the task is decomposed into perception, estimation, planning, and control, we are proposing a behavioral cloning approach. In this method, a convolutional neural network is trained with the flights of a human operator. So that the output of the trained network is directly the desired MAV state so that it leads the drone through the gate. We have tested the method using a validation set where we obtained a low loss. Furthermore, we have tested the trained network with unseen data obtaining promising results.
AB - Drone racing presents a challenge to autonomous micro aerial vehicles (MAV) because usually the track is not known in advance and it is affected by the environment light. In such scenarios, the vehicle has to act quickly depending on the information provided by its sensors. In this work, we want to predict the movement of the drone so that it passes through a gate. Unlike previous approaches where the task is decomposed into perception, estimation, planning, and control, we are proposing a behavioral cloning approach. In this method, a convolutional neural network is trained with the flights of a human operator. So that the output of the trained network is directly the desired MAV state so that it leads the drone through the gate. We have tested the method using a validation set where we obtained a low loss. Furthermore, we have tested the trained network with unseen data obtaining promising results.
UR - http://www.scopus.com/inward/record.url?scp=85071872420&partnerID=8YFLogxK
U2 - 10.1109/ICUAS.2019.8798172
DO - 10.1109/ICUAS.2019.8798172
M3 - Contribución a la conferencia
AN - SCOPUS:85071872420
T3 - 2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019
SP - 1353
EP - 1358
BT - 2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019
Y2 - 11 June 2019 through 14 June 2019
ER -