Adaptive data transfer methods via policy evolution for UAV swarms

Smith, Phillip; Hunjed, Robert; Aleti, Aldeida; Barca, Jan Carlo

doi:10.1109/atnac.2017.8215356

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…With popularity of online learning and AI algorithms, some researchers have used such techniques in swarm networks. For example, in [31] an online reinforcement learning (RL) scheme is used for transferring time-insensitive packets in sparse networks, where UAVs help to transfer data. Inspired by geographical routing, it defines a reward function and learns whether to relay data among UAVs or move UAVs to a new location.…”

Section: Related Workmentioning

confidence: 99%

Skeleton-Based Swarm Routing (SSR): Intelligent Smooth Routing for Dynamic UAV Networks

Toorchi

Bentley

et al. 2021

IEEE Access

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Section: Related Workmentioning

confidence: 99%

Skeleton-Based Swarm Routing (SSR): Intelligent Smooth Routing for Dynamic UAV Networks

Toorchi

Bentley

et al. 2021

IEEE Access

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“…This model estimates the signal power loss over distance, determining if a communication attempt is successful. For a more in-depth discussion of the swarm agent operations the reader is referred to Smith et al (2017) and Smith et al (2018b).…”

Section: Networking Taskmentioning

confidence: 99%

Robotic Hierarchical Graph Neurons. A novel implementation of HGN for swarm robotic behaviour control

Smith,

Aleti,

Lee

et al. 2019

Preprint

Self Cite

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This paper explores the use of a novel form of Hierarchical Graph Neurons (HGN) for in-operation behaviour selection in a swarm of robotic agents. This new HGN is called Robotic-HGN (R-HGN), as it matches robot environment observations to environment labels via fusion of match probabilities from both temporal and intra-swarm collections. This approach is novel for HGN as it addresses robotic observations being pseudo-continuous numbers, rather than categorical values. Additionally, the proposed approach is memory and computation-power conservative and thus is acceptable for use in mobile devices such as single-board computers, which are often used in mobile robotic agents. This R-HGN approach is validated against individual behaviour implementation and random behaviour selection. This contrast is made in two sets of simulated environments: environments designed to challenge the held behaviours of the R-HGN, and randomly generated environments which are more challenging for the robotic swarm than R-HGN training conditions. R-HGN has been found to enable appropriate behaviour selection in both these sets, allowing significant swarm performance in pretrained and unexpected environment conditions.

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Investigation on vulnerabilities, threats and attacks prohibiting UAVs charging and depleting UAVs batteries: Assessments & countermeasures

et al. 2022

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Adaptive data transfer methods via policy evolution for UAV swarms

Cited by 9 publications

References 30 publications

Skeleton-Based Swarm Routing (SSR): Intelligent Smooth Routing for Dynamic UAV Networks

Skeleton-Based Swarm Routing (SSR): Intelligent Smooth Routing for Dynamic UAV Networks

Robotic Hierarchical Graph Neurons. A novel implementation of HGN for swarm robotic behaviour control

Investigation on vulnerabilities, threats and attacks prohibiting UAVs charging and depleting UAVs batteries: Assessments & countermeasures

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