Remote UAV Online Path Planning via Neural Network-Based Opportunistic Control
| Autor: | Hamid Mohammad Shiri, Jihong Park, Mehdi Bennis |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
communication-efficient online path planning
0209 industrial biotechnology Artificial neural network Computer science Real-time computing Hamilton–Jacobi–Bellman equation 020206 networking & telecommunications 02 engineering and technology Base station Upload machine learning 020901 industrial engineering & automation Control and Systems Engineering Robustness (computer science) Telecommunications link 0202 electrical engineering electronic engineering information engineering Train Motion planning Remote UAV control Electrical and Electronic Engineering |
| Zdroj: | IEEE Wireless Communications Letters. 9:861-865 |
| ISSN: | 2162-2345 2162-2337 |
| DOI: | 10.1109/lwc.2020.2973624 |
| Popis: | This letter proposes a neural network (NN) aided remote unmanned aerial vehicle (UAV) online control algorithm, coined oHJB. By downloading a UAV’s state, a base station (BS) trains an HJB NN that solves the Hamilton-Jacobi-Bellman equation (HJB) in real time, yielding a sub-optimal control action. Initially, the BS uploads this control action to the UAV. If the HJB NN is sufficiently trained and the UAV is far away, the BS uploads the HJB NN model, enabling to locally carry out control decisions even when the connection is lost. Simulations corroborate the effectiveness of oHJB in reducing the UAV’s travel time and energy by utilizing the trade-off between uploading delays and control robustness in poor channel conditions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|