TY - GEN
T1 - An Efficient Deep Reinforcement Learning Framework for UAVs
AU - Zhou, Shanglin
AU - Li, Bingbing
AU - Ding, Caiwu
AU - Lu, Lu
AU - Ding, Caiwen
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - 3D Dynamic simulator such as Gazebo has become a popular substitution for unmanned aerial vehicle (UAV) because of its user-friendly in real-world scenarios. At this point, well-functioning algorithms on the UAV controller are needed for guidance, navigation, and control for autonomous navigation. Deep reinforcement learning (DRL) comes into sight as its famous self-learning characteristic. This goal-orientated algorithm can learn how to attain a complex objective or maximize along a particular dimension over many steps. In this paper, we propose a general framework to incorporate DRL with the UAV simulation environment. The whole system consists of the DRL algorithm for attitude control, packing algorithm on the Robot Operation System (ROS) to connect DRL with PX4 controller, and a Gazebo simulator that emulates the real-world environment. Experimental results demonstrate the effectiveness of the proposed framework.
AB - 3D Dynamic simulator such as Gazebo has become a popular substitution for unmanned aerial vehicle (UAV) because of its user-friendly in real-world scenarios. At this point, well-functioning algorithms on the UAV controller are needed for guidance, navigation, and control for autonomous navigation. Deep reinforcement learning (DRL) comes into sight as its famous self-learning characteristic. This goal-orientated algorithm can learn how to attain a complex objective or maximize along a particular dimension over many steps. In this paper, we propose a general framework to incorporate DRL with the UAV simulation environment. The whole system consists of the DRL algorithm for attitude control, packing algorithm on the Robot Operation System (ROS) to connect DRL with PX4 controller, and a Gazebo simulator that emulates the real-world environment. Experimental results demonstrate the effectiveness of the proposed framework.
KW - Deep Reinforcement Learning
KW - Gazebo
KW - PX4
KW - Simulation Environment
KW - Unmanned Aerial Vehicle
UR - http://www.scopus.com/inward/record.url?scp=85089938371&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089938371&partnerID=8YFLogxK
U2 - 10.1109/ISQED48828.2020.9136980
DO - 10.1109/ISQED48828.2020.9136980
M3 - Conference contribution
AN - SCOPUS:85089938371
T3 - Proceedings - International Symposium on Quality Electronic Design, ISQED
SP - 323
EP - 328
BT - Proceedings of the 21st International Symposium on Quality Electronic Design, ISQED 2020
PB - IEEE Computer Society
T2 - 21st International Symposium on Quality Electronic Design, ISQED 2020
Y2 - 25 March 2020 through 26 March 2020
ER -