H. Shiri, J. Park and M. Bennis, "Remote UAV Online Path Planning via Neural Network-Based Opportunistic Control," in IEEE Wireless Communications Letters, vol. 9, no. 6, pp. 861-865, June 2020, doi: 10.1109/LWC.2020.2973624
Remote UAV online path planning via neural network-based opportunistic control
|Author:||Shiri, Hamid1; Park, Jihong1; Bennis, Mehdi1|
1University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020100983556
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-10-09
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.
IEEE wireless communications letters
|Pages:||861 - 865|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
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