A fault-tolerant self-organizing flocking approach for UAV aerial survey

Benedetti, Massimiliano De; D'Urso, F.; Fortino, Giancarlo; Messina, Fabrizio; Pappalardo, Giuseppe; Santoro, Corrado

doi:10.1016/j.jnca.2017.08.004

Cited by 41 publications

(19 citation statements)

References 20 publications

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“…Critical Analysis: In the existing literature [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , various drone-based approaches are proposed for smart healthcare or mission-oriented systems. This approach can be realized from COVID-19 pandemic situations as well.…”

Section: Related Workmentioning

confidence: 99%

A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic

Kumar

Sharma

Singh

et al. 2021

Future Generation Computer Systems

216

143

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Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. It is similar to influenza viruses and raises concerns through alarming levels of spread and severity resulting in an ongoing pandemic worldwide. Within eight months (by August 2020), it infected 24.0 million persons worldwide and over 824 thousand have died. Drones or Unmanned Aerial Vehicles (UAVs) are very helpful in handling the COVID-19 pandemic. This work investigates the drone-based systems, COVID-19 pandemic situations, and proposes an architecture for handling pandemic situations in different scenarios using real-time and simulation-based scenarios. The proposed architecture uses wearable sensors to record the observations in Body Area Networks (BANs) in a push–pull data fetching mechanism. The proposed architecture is found to be useful in remote and highly congested pandemic areas where either the wireless or Internet connectivity is a major issue or chances of COVID-19 spreading are high. It collects and stores the substantial amount of data in a stipulated period and helps to take appropriate action as and when required. In real-time drone-based healthcare system implementation for COVID-19 operations, it is observed that a large area can be covered for sanitization, thermal image collection, and patient identification within a short period (2 KMs within 10 min approx.) through aerial route. In the simulation, the same statistics are observed with an addition of collision-resistant strategies working successfully for indoor and outdoor healthcare operations. Further, open challenges are identified and promising research directions are highlighted.

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Section: Related Workmentioning

confidence: 99%

A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic

Kumar

Sharma

Singh

et al. 2021

Future Generation Computer Systems

216

143

View full text Add to dashboard Cite

show abstract

“…The authors in [19] proposed a multi-UAV control model that could increase the deployment coverage of UAVs with energy efficiency. [20] proposed a decentralized solution for multi-UAV deployment by adjusting a set of parameters that control the UAV's behaviors and actions. [21] studied to minimize the deployment delay of all UAVs and the overall delay.…”

Section: Uav Deployment and Uav Ad-hoc Networkmentioning

confidence: 99%

“…To this end, we tested the performance of DRL methods and heuristic baselines with the number of UAVs varies within [5,10,20,40,60,80].…”

Section: Scalability and Transfer-abilitymentioning

confidence: 99%

Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning

Ye¹

2021

Preprint

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<div>In this paper, we aim to design a deep reinforcement learning(DRL) based control solution to navigate a swarm of unmanned aerial vehicles (UAVs) to fly around an unexplored target area under provide optimal communication coverage for the ground mobile users. Compared with existing DRL-based solutions that mainly solve the problem with global observation and centralized training, a practical and efficient Decentralized Training and Decentralized Execution(DTDE) framework is desirable to train and deploy each UAV in a distributed manner. To this end, we propose a novel DRL approach named Deep Recurrent Graph Network(DRGN) that makes use of Graph Attention Network-based Flying Ad-hoc Network(GAT-FANET) to achieve inter-UAV communications and Gated Recurrent Unit (GRU) to record historical information. We conducted extensive experiments to define an appropriate structure for GAT-FANET and examine the performance of DRGN. The simulation results show that the proposed model outperforms four state-of-the-art DRL-based approaches and four heuristic baselines, and demonstrate the scalability, transferability, robustness, and interpretability of DRGN.</div>

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“…However, the leader can easily become a point of failure of the entire swarm due to its control [21]. A more flexible schema is Flocking, which is still based on mutual distances among drones, but with a dynamic rearrangement capability based on: (i) maintaining the heading of flockmates, (ii) avoiding collisions and (iii) attempting to remain in range between them [11], [22], [12], and [23].…”

Section: Related Workmentioning

confidence: 99%

Design and simulation of the emergent behavior of small drones swarming for distributed target localization

Alfeo

Cimino

Francesco

et al. 2018

Journal of Computational Science

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A fault-tolerant self-organizing flocking approach for UAV aerial survey

Cited by 41 publications

References 20 publications

A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic

A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic

Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning

Design and simulation of the emergent behavior of small drones swarming for distributed target localization

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