Swarm learning in restricted environments: an examination of semi-stochastic action selection

Smith, Phillip; Hunjet, Robert; Khan, Asad I.

doi:10.1109/icarcv.2018.8581269

Cited by 4 publications

(10 citation statements)

References 23 publications

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“…Agents travel towards other swarm members or packet destinations without the even spacing of MB-1. Upon obstacle detection, the movement profile is dominated by repulsive and orbital virtual forces (Rezaee and Abdollahi, 2014;Chang et al, 2003) relative to the obstacle, as prior seen in Smith et al (2018b). This motion profile continues until a closer obstacle is detected, the target is in communication range, or the agents are unable to move without collision.…”

Section: Designed Behavioursmentioning

confidence: 89%

“…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%

“…Recent advancements in swarm robotic behaviour creation has seen an increase in task effectiveness for swarm robotics in non-trivial tasks, such as data-transfer via Mobile Ad-hoc NETworks (MANETs) creation (Smith et al, 2018b). However, this behaviour evolution approach suffers from poor transferability between environments and problem instances.…”

Section: Introductionmentioning

confidence: 99%

“…The exploration of this R-HGN in swarm behaviour switching is conducted in this paper via simulations of a data-transfer task seen in Smith et al (2018b). This task is partiallyobservable, and requires behaviour heterogeneity both across the swarm and across task duration.…”

Section: Introductionmentioning

confidence: 99%

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

Smith,

Aleti,

Lee

et al. 2019

Preprint

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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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Section: Designed Behavioursmentioning

confidence: 89%

Section: Networking Taskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Smith,

Aleti,

Lee

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ODM ensemble, the heterogeneous sensors connected within the individual IoT module and the dynamics of each pair of heterogeneous sensors was modelled. The multi-agent deep reinforcement learning uses multi-agents in learning and transfers learning between the agents [50,51]. Consider a set of heterogeneous sensors connected to a time-invariant IoT system.…”

Section: Proposed Iot Sensor's Outlier Detection Modelmentioning

confidence: 99%

Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions

et al. 2020

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The Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world in this Industry 4.0 era. The IoTs are being used in many diverse applications that are part of our life and is growing to become the global digital nervous systems. It is quite evident that in the near future, hundreds of millions of individuals and businesses with billions will have smart-sensors and advanced communication technology, and these things will expand the boundaries of current systems. This will result in a potential change in the way we work, learn, innovate, live and entertain. The heterogeneous smart sensors within the Internet of Things are indispensable parts, which capture the raw data from the physical world by being the first port of contact. Often the sensors within the IoT are deployed or installed in harsh environments. This inevitably means that the sensors are prone to failure, malfunction, rapid attrition, malicious attacks, theft and tampering. All of these conditions cause the sensors within the IoT to produce unusual and erroneous readings, often known as outliers. Much of the current research has been done in developing the sensor outlier and fault detection models exclusively for the Wireless Sensor Networks (WSN), and adequate research has not been done so far in the context of the IoT. Wireless sensor network’s operational framework differ greatly when compared to IoT’s operational framework, using some of the existing models developed for WSN cannot be used on IoT’s for detecting outliers and faults. Sensor faults and outlier detection is very crucial in the IoT to detect the high probability of erroneous reading or data corruption, thereby ensuring the quality of the data collected by sensors. The data collected by sensors are initially pre-processed to be transformed into information and when Artificially Intelligent (AI), Machine Learning (ML) models are further used by the IoT, the information is further processed into applications and processes. Any faulty, erroneous, corrupted sensor readings corrupt the trained models, which thereby produces abnormal processes or outliers that are significantly distinct from the normal behavioural processes of a system. In this paper, we present a comprehensive review of the detecting sensor faults, anomalies, outliers in the Internet of Things and the challenges. A comprehensive guideline to select an adequate outlier detection model for the sensors in the IoT context for various applications is discussed.

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Data Communication Assistance via Swarm Robotic: A Behaviour Creation Comparison

Smith

Khan

Aleti

et al. 2020

Lecture Notes in Computer Science

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This paper compares two behaviour creation algorithms for Unmanned Aerial Vehicle swarms. The objective of this work is to have a self-organising robotic swarm which unintrusively assists humans in network restricted environments by facilitating data-transfer between disconnected groups. The behaviour creation algorithms explored are a well-cited approach for evolving neural networks (ENN) and our Learning Classifier System (LCS) approach. We utilise simulations in randomised, obstacle dense landscapes to validate, compare and contrast these two algorithms. We explore the algorithms both with and without the environment layout being known to the swarm a priori. These simulations show the ENN algorithm struggles to create appropriate behaviours for this complex data-transfer task, resulting in an unassertive swarm. Our LCS behaviours have the swarm operate with up to 61% of the fitness range above ENN in both expected and unexpected environments, resulting in the desired human assistance.

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Swarm learning in restricted environments: an examination of semi-stochastic action selection

Cited by 4 publications

References 23 publications

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

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

Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions

Data Communication Assistance via Swarm Robotic: A Behaviour Creation Comparison

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