TY - GEN
T1 - Trajectory generation for networked UAVs using online learning for delay compensation
AU - Yoo, Jaehyun
AU - Lee, Seungjae
AU - Kim, H. Jin
AU - Johansson, Karl H.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/6
Y1 - 2017/10/6
N2 - This paper presents a trajectory generation mechanism based on machine learning for a network of unmanned aerial vehicles (UAVs). For delay compensation, we apply an online regression technique to learn a pattern of network-induced effects on UAV maneuvers. Due to online learning, the control system not only adapts to changes to the environment, but also maintains a fixed amount of training data. The proposed algorithm is evaluated on a collaborative trajectory tracking task for two UAVs. Improved tracking is achieved in comparison to a conventional linear compensation algorithm.
AB - This paper presents a trajectory generation mechanism based on machine learning for a network of unmanned aerial vehicles (UAVs). For delay compensation, we apply an online regression technique to learn a pattern of network-induced effects on UAV maneuvers. Due to online learning, the control system not only adapts to changes to the environment, but also maintains a fixed amount of training data. The proposed algorithm is evaluated on a collaborative trajectory tracking task for two UAVs. Improved tracking is achieved in comparison to a conventional linear compensation algorithm.
UR - https://www.scopus.com/pages/publications/85047635778
U2 - 10.1109/CCTA.2017.8062740
DO - 10.1109/CCTA.2017.8062740
M3 - Conference contribution
AN - SCOPUS:85047635778
T3 - 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017
SP - 1941
EP - 1946
BT - 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017
Y2 - 27 August 2017 through 30 August 2017
ER -