Trajectory Planning for Communication Relay Unmanned Aerial Vehicles in Urban Dynamic Environments

Ładosz, Paweł; Oh, Hyondong; Chen, Wenhua

doi:10.1007/s10846-017-0484-y

Cited by 29 publications

(13 citation statements)

References 17 publications

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“…Drones may also provide communication to ground vehicles. For instance, as communication relays, they may connect vehicles, for example, rescue vehicles, to the ground control station and with each other .…”

Section: Planning Combined Operations Of Drones With Other Vehiclesmentioning

confidence: 99%

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

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Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult‐to‐access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

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Section: Planning Combined Operations Of Drones With Other Vehiclesmentioning

confidence: 99%

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

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“…wherex pos ∈ R 3×n andx g,pos ∈ R 3×m represent the position of UAVs and ground nodes, respectively. It is worth noting that some other performance metrics could also be used such as the worst case connectivity or modified GMC [6].…”

Section: Communication Performance Metricsmentioning

confidence: 99%

A Hybrid Approach of Learning and Model-Based Channel Prediction for Communication Relay UAVs in Dynamic Urban Environments

Ładosz

Zheng

et al. 2019

IEEE Robot. Autom. Lett.

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This paper presents the trajectory planning of small UAVs for a communication relay mission in an urban environment. In particular, we focus on predicting the communication strength between air and ground nodes accurately to allow relay UAVs to maximise the communication performance improvement of networked nodes. In urban environments, this prediction is not easily achievable even with good mathematical models as each model is characterized by a series of parameters which are not trivial to obtain or estimate apriori and can vary during the mission. To address the difficulty, this work proposes to integrate a learning-based measurement technique with a probabilistic communication channel model. This hybrid approach is able to predict communication model parameters based on signal strength data which UAVs observe during the mission online, thus achieving better performance compared with the model-based approach in an urban environment. The predicted parameters are based on four discrete urban environment types. Numerical simulations validate the performance and benefit of the proposed approach. Index Terms-Aerial Systems: Applications, Learning and Adaptive Systems, Motion and Path Planning I. INTRODUCTION E STABLISHING wireless communication networks is vital in various circumstances such as rescue missions in a natural disaster. In urban environments, wireless communication is limited by the short coverage range and small bandwidth due to line-of-sight (LOS) obstruction by buildings. The following technologies have been used as an attempt to mitigate issues in urban environments: infrastructures (e.g. cell

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“…One of key disadvantage of this metric is the fact that it can sometimes ignore very weak connections, to improve a group of already good ones if sum of them would bring bigger benefit than improving weak one. To mitigate that other communication performance metrics could have been used such as worst case connectivity or modified global message connectivity [12]. First one would make UAV improve weakest connection while second one would look to improve a set of connections of strength below pre-specified threshold.…”

Section: B Communication Performance Metricmentioning

confidence: 99%

“…Finally, as the NMPC-based approach only optimises up to a certain length (rather than a full scenario), the problem space is reduced, leading to the less computational time. For more details, readers are referred to [12].…”

Section: Nmpc-based Trajectory Planner For Uav Communication Relaymentioning

confidence: 99%

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Prediction of air-to-ground communication strength for relay UAV trajectory planner in urban environments

Ładosz

Chen

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This 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.

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Trajectory Planning for Communication Relay Unmanned Aerial Vehicles in Urban Dynamic Environments

Cited by 29 publications

References 17 publications

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

A Hybrid Approach of Learning and Model-Based Channel Prediction for Communication Relay UAVs in Dynamic Urban Environments

Prediction of air-to-ground communication strength for relay UAV trajectory planner in urban environments

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