Deep Reinforcement Learning of Collision-Free Flocking Policies for Multiple Fixed-Wing UAVs Using Local Situation Maps

Yan, Chao; Wang, Chang; Xiang, Xiaojia; Lan, Zhen; Jiang, Yuna

doi:10.1109/tii.2021.3094207

Cited by 44 publications

(12 citation statements)

References 29 publications

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“…Resource allocation in UAV networks needs to be done carefully. A reinforcement learning method for resource allocation is investigated by Luong et al 91 Mou et al 92 studied an RL-based method for improving coverage, whereas the collision avoidance of UAVs was the topic of study in Yan et al 93 To improve the capacity, power efficiency, coverage, and for the efficient utilization of the available spectrum, optimization of the constraints is must. As reflected in Table 13, the more devices participate in a network, such as in the case of mMTC, the higher the security concerns.…”

Section: Integration With Aimentioning

confidence: 99%

“…AI in wireless communication is incorporated by infusing machine learning techniques into the design paradigms. The research works of Bayerlein et al, 84 Huang et al, 85 Bayerlein et al, 86 Liu et al, 87,88 Wang et al, 89 Mahmud et al, 90 Luong et al, 91 Mou et al, 92 and Yan et al 93 had focused on reinforcement learning with next generation wireless communication.…”

Section: Iotmentioning

confidence: 99%

Section: Iotmentioning

confidence: 99%

“…Section “Optimization techniques” provides the review on integration of wireless communication with AI. 81–93…”

Section: Introductionmentioning

confidence: 99%

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Unmanned aerial vehicles: Applications, techniques, and challenges as aerial base stations

Rolly

Malarvezhi

Λάγκας

2022

International Journal of Distributed Sensor Networks

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Next generation wireless networks are expected to be greatly supported by unmanned aerial vehicles, which can act as aerial base stations and constitute a promising solution for the exorbitant rise in user demands. This is possible because of unmanned aerial vehicle characteristics such as mobility, flexibility, increased line-of-sight probability, and their ability to access unreachable locations. Extensive research is now widely performed on the deployment, performance analysis, resource management, trajectory optimization, and channel modeling in such networks. This survey article focuses on the different applications and the related algorithms for realizing aerial base stations by thoroughly reviewing each related research area. In a nutshell, this article provides key applications, challenges, and the technology used for the design and analysis of unmanned aerial vehicles as base stations.

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Section: Integration With Aimentioning

confidence: 99%

Section: Iotmentioning

confidence: 99%

Section: Iotmentioning

confidence: 99%

“…Section “Optimization techniques” provides the review on integration of wireless communication with AI. 81–93…”

Section: Introductionmentioning

confidence: 99%

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Unmanned aerial vehicles: Applications, techniques, and challenges as aerial base stations

Rolly

Malarvezhi

Λάγκας

2022

International Journal of Distributed Sensor Networks

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“…Self-organization means that any global motion mode results from local decisions. The decisions of each agent are only related to a limited number of neighbors, independent of the aerial swarm size [13]. This is highly critical for drone swarm control, which means that we can deploy dozens or hundreds of drones with limited computing resources.…”

Section: Introductionmentioning

confidence: 99%

AGDS: adaptive goal-directed strategy for swarm drones flying through unknown environments

Wang

Huang

Low

et al. 2022

Complex Intell. Syst.

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This paper aims to address a challenging problem of a drone swarm for a specific mission by reaching a desired region, through an unknown environment. A bio-inspired flocking algorithm with adaptive goal-directed strategy (AGDS) is proposed and developed for the drones swarmed across unknown environments. Each drone employs a biological visual mechanism to sense obstacles in within local perceptible scopes. Task information of the destination is only given to a few specified drones (named as informed agents), rather than to all other individual drones (uninformed agents). With the proposed flocking swarm, the informed agents operate collectively with the remaining uninformed agents to achieve a common and overall mission. By virtue of numerical simulation, the AGDS and non-adaptive goal-directed strategy (non-AGDS) are both presented and evaluated. Experiments by flying six DJI Tello quadrotors indoor are conducted to validate the developed flocking algorithm. Additional validations within canyon-like complicated scenarios have also been carried out. Both simulation and experimental results demonstrate the efficiency of the proposed swarm flocking algorithm with AGDS.

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A Review on Smart Navigation Techniques for Automated Vehicle

Haldorai,

Murugan

et al. 2024

Artificial Intelligence for Sustainable Development

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Deep Reinforcement Learning of Collision-Free Flocking Policies for Multiple Fixed-Wing UAVs Using Local Situation Maps

Cited by 44 publications

References 29 publications

Unmanned aerial vehicles: Applications, techniques, and challenges as aerial base stations

Unmanned aerial vehicles: Applications, techniques, and challenges as aerial base stations

AGDS: adaptive goal-directed strategy for swarm drones flying through unknown environments

A Review on Smart Navigation Techniques for Automated Vehicle

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