Real-time dynamic planning to maintain network connectivity in a team of unmanned air vehicles

Kopeikin, Andrew; Ponda, Sameera S.; Johnson, Luke B.; Toupet, Olivier; How, Jonathan P.

doi:10.1109/glocomw.2011.6162396

Cited by 12 publications

(9 citation statements)

References 11 publications

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“…Moreover, UAVs have limited on-board batteries and are subject to strict flight regulation rules and weather conditions. Therefore, minimizing the flight time, while meeting the demands needed to optimize the service performance, is another challenge in U-RANs [50][51][52][53]. For instance, in U-RANs supporting different applications, the choice of the type of UAV depends on payload, data generating period, delay sensitivity, and required amount of data processing.…”

Section: Payload and Flight-time Constraintsmentioning

confidence: 99%

On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks

et al. 2020

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A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies.

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Section: Payload and Flight-time Constraintsmentioning

confidence: 99%

On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks

et al. 2020

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“…The objective of the planner is to maximize reward for the mission by assigning the right asset, to the right task, at the right time. 32 The task allocation in this work can be formulated as the following optimization problem:…”

Section: Iiia Decentralized Task Allocationmentioning

confidence: 99%

Flight Testing a Heterogeneous Multi-UAV System with Human Supervision

Kopeikin

Clare

Toupet

et al. 2012

AIAA Guidance, Navigation, and Control Conference

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This paper presents the outdoor flight test results of a decentralized multi-UAV system supervised by a human operator. The system balances the roles of the human operator and the UAV autonomous behaviors with the objective of maximizing the execution performance. The operator manages the mission by inputting and modifying tasks instead of controlling individual UAVs. The Consensus-Based Bundle Algorithm (CBBA) is used as a real-time, scalable, dynamic multi-agent multi-task planning algorithm to allocate tasks approved by the operator to UAVs. A team of three quadrotors and one fixed wing UAV collaborated in an operationally relevant scenario supporting a cargo UAV resupply mission. Thirteen of fourteen multi-UAV outdoor flight test trials successfully accomplished the mission objectives. The framework was shown to be robust to system failures and degradations commonly encountered during field testing primarily because of health monitoring and management tools that were incorporated in the design. Instances of task allocation and path planning churning were observed which are linked to uncertainties of operating outdoors. Lessons learned during flight test operations are highlighted as they are relevant to other similar types of systems and missions.

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“…The nodes also use the concept of agents to distribute the tasks in the mission. For this the authors use the Consensus Based Bundle Algorithm (CBBA) [19] that runs on the BS, where the nodes bid the task according to their capabilities. Agents receive rewards when they complete tasks and then the mission.…”

Section: E Shortest-path Based Protocolsmentioning

confidence: 99%

Unmanned Aerial Vehicle Networking Protocols

Suescún¹,

Cardei²

2016

Proceedings of the 14th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Engineering Innovati

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Unmanned Aerial vehicles (UAV), more commonly known as drones, are widely used by military and civilian applications. They can be used for remote sensing, transportation, scientific research, armed attacks, search, and rescue. They can be used in applications where human presence is dangerous. Drones could be equipped with cameras, sensors, communications equipment, and weapons. UAVs can be piloted by a human from land or can be autonomous. In the future, autonomous UAVs will work collaboratively with other aircrafts. UAVs can communicate wirelessly, forming a MANET. Thus the constraints and advantages of MANETs are transferable to networks of UAV. This paper presents some networking protocols for the UAV wireless networks.

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Real-time dynamic planning to maintain network connectivity in a team of unmanned air vehicles

Cited by 12 publications

References 11 publications

On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks

On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks

Flight Testing a Heterogeneous Multi-UAV System with Human Supervision

Unmanned Aerial Vehicle Networking Protocols

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