IROS_draft_1.pdf (2.06 MB)
Prediction of air-to-ground communication strength for relay UAV trajectory planner in urban environments
conference contribution
posted on 2018-03-02, 12:11 authored by Pawel Ladosz, Hyondong Oh, Wen-Hua ChenWen-Hua ChenThis paper proposes the use of a learning approach to predict air-to-ground (A2G) communication strength in support of the communication relay mission using UAVs in an
urban environment. To plan an efficient relay trajectory, A2G communication link quality needs to be predicted between the UAV and ground nodes. However, due to frequent occlusions by buildings in the urban environment, modelling and predicting
communication strength is a difficult task. Thus, a need for learning techniques such as Gaussian Process (GP) arises to learn about inaccuracies in a pre-defined communication
model and the effect of line-of-sight obstruction. Two ways of combining GP with a relay trajectory planner are presented:
i) scanning the area of interest with the UAV to collect communication strength data first and then using learned data in the trajectory planner and ii) collecting data and running
the trajectory planner simultaneously. The performance of both approaches is compared with Monte Carlo simulations. It is shown that the first implementation results in slightly better predictions, however the second one benefits from being able to start the relay mission immediately.
Funding
This work was supported by the UK Engineering and Physical Science Research Council (EPSRC) under the Grant EP/J011525/1.
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering
Published in
International Conference On Intelligent Robots and SystemsCitation
LADOSZ, P., OH, H. and CHEN, W-H., 2017. Prediction of air-to-ground communication strength for relay UAV trajectory planner in urban environment. Presented at the 2017 IEEE/RSJ International Conference On Intelligent Robots and Systems (IROS 2017), Vancouver, Canada, 24-28 September 2017, pp.6831-6836.Publisher
© IEEEVersion
- AM (Accepted Manuscript)
Acceptance date
2017-06-14Publication date
2017Notes
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.ISBN
978153862682517eISSN
2153-0866Publisher version
Language
- en