Deep Reinforcement Learning for Persistent Cruise Control in UAV-aided Data Collection
Ref: CISTER-TR-211008 Publication Date: 4, Oct, 2021
Deep Reinforcement Learning for Persistent Cruise Control in UAV-aided Data CollectionRef: CISTER-TR-211008 Publication Date: 4, Oct, 2021
Autonomous UAV cruising is gaining attention due to its flexible deployment in remote sensing, surveillance, and reconnaissance. A critical challenge in data collection with the autonomous UAV is the buffer overflows at the ground sensors and packet loss due to lossy airborne channels. Trajectory planning of the UAV is vital to alleviate buffer overflows as well as channel fading. In this work, we propose a Deep Deterministic Policy Gradient based Cruise Control (DDPG-CC) to reduce the overall packet loss through online training of headings and cruise velocity of the UAV, as well as the selection of the ground sensors for data collection. Preliminary performance evaluation demonstrates that DDPG-CC reduces the packet loss rate by under 5% when sufficient training is provided to the UAV.
The IEEE Local Computer Networks conference (LCN).