Deep Reinforcement Learning for Persistent Cruise Control in UAV-aided Data Collection
Autonomous UAV cruising is gaining attention dueto its flexible deployment in remote sensing, surveillance, andreconnaissance. A critical challenge in data collection with theautonomous UAV is the buffer overflows at the ground sensorsand packet loss due to lossy airborne channels. Trajectoryplannin...
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| Outros Autores: | , , , |
| Formato: | conferenceObject |
| Idioma: | eng |
| Publicado em: |
2021
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.22/18804 |
| País: | Portugal |
| Oai: | oai:recipp.ipp.pt:10400.22/18804 |
| Resumo: | Autonomous UAV cruising is gaining attention dueto its flexible deployment in remote sensing, surveillance, andreconnaissance. A critical challenge in data collection with theautonomous UAV is the buffer overflows at the ground sensorsand packet loss due to lossy airborne channels. Trajectoryplanning of the UAV is vital to alleviate buffer overflows as wellas channel fading. In this work, we propose a Deep DeterministicPolicy Gradient based Cruise Control (DDPG-CC) to reducethe overall packet loss through online training of headings andcruise velocity of the UAV, as well as the selection of the groundsensors for data collection. Preliminary performance evaluationdemonstrates that DDPG-CC reduces the packet loss rate byunder 5% when sufficient training is provided to the UAV. |
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