Dynamic Collaboration Between Networked Robots and Clouds in Resource-Constrained Environments

Pandey, Parul; Pompili, Dario; Yi, Jingang

doi:10.1109/tase.2015.2406115

Cited by 39 publications

(16 citation statements)

References 11 publications

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“…On sloping beaches the limitation by the water depth is a major factor shaping the wave properties through bottom friction, depth-induced wave breaking, and shallow water non-linear wave interactions (SPM, 1984;Eldeberky, 1996). Coastal coral reefs shape the wave spectrum away from the deep-water form (Hardy and Young, 1996); the same thing can be said for tidal inlets where the waves are also affected by strong currents (van der Westhuysen et al, 2012).…”

Section: Introductionmentioning

confidence: 88%

The wave spectrum in archipelagos

Björkqvist¹,

Pettersson²,

Kahma³

2019

Preprint

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Abstract. Sea surface waves are important for marine safety and coastal constructions, but mapping the wave properties at complex shorelines, such as coastal archipelagos, is challenging. The wave spectrum, E(f), contains a majority of the information about the wave field, and its properties have been studied for decades. Nevertheless, any systematic research into the wave spectrum in archipelagos has not been made. In this paper we present wave buoy measurements from 14 locations in the Finnish archipelago. The shape of the wave spectrum showed a systematic transition from a single peaked spectrum, to a spectrum with a wide frequency range having almost constant energy. The exact shape also depended on the wind direction, since the fetch, island, and bottom conditions are not isotropic. The deviation from the traditional spectral form is strong enough to have a measurable effect on the definitions of the significant wave height. The relation between the two definitions in the middle of the archipelago was H1/3 = 0.881 Hs, but the ratio varied with the spectral width (Hs was defined using the variance). At this same location the average value of the single highest wave, Hmax/Hs, was only 1.58. A wider archipelago spectrum was also associated with lower confidence limits for the significant wave height compared to the open sea (6 % vs. 9 %). The challenges regarding the instability of the peak frequency and the difficulties in finding a good characteristic frequency for an archipelago spectrum is discussed. The mean frequency, weighted with E(f)4, is proposed as a compromise between stability, and bias with respect to the peak frequency.

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Section: Introductionmentioning

confidence: 88%

The wave spectrum in archipelagos

Björkqvist¹,

Pettersson²,

Kahma³

2019

Preprint

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“…The authors in [42] introduced a communication framework for Autonomous Underwater Vehicles (AUVs) which expands the computational and data resources that are available to a team of AUVs by including public cloud computing resources. A resource provisioning engine is designed to offload some of the AUVs' tasks to the cloud datacenter.…”

Section: Related Literaturementioning

confidence: 99%

“…Learning land robots [42] Enhance robot learning through cooperation The solution is based on the peer-assisted learning theory and reciprocal learning strategy.…”

Section: Reduced Execution Delaymentioning

confidence: 99%

Cloud-Based Multi-Agent Cooperation for IoT Devices Using Workflow-Nets

et al. 2019

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Most Internet of Things (IoT)-based service requests require excessive computation which exceeds an IoT device's capabilities. Cloud-based solutions were introduced to outsource most of the computation to the data center. The integration of multiagent IoT systems with cloud computing technology makes it possible to provide faster, more efficient and real-time solutions. Multi-agent cooperation for distributed systems such as fog-based cloud computing has gained popularity in contemporary research

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“…Related work has shown that integrating with a cloud can indeed provide benefits to autonomous vehicles. In [12], remote resources such as terrestrial antennas and cloud infrastructures are added to a local unmanned vehicle team (here, underwater vehicles). Computationally-extensive tasks are executed in the cloud.…”

Section: Related Workmentioning

confidence: 99%

A Distributed Architecture for Human-Drone Teaming: Timing Challenges and Interaction Opportunities

Hummel

Pollak

Krahofer

2019

Sensors

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Drones are expected to operate autonomously, yet they will also interact with humans to solve tasks together. To support civilian human-drone teams, we propose a distributed architecture where sophisticated operations such as image recognition, coordination with humans, and flight-control decisions are made, not on-board the drone, but remotely. The benefits of such an architecture are the increased computational power available for image recognition and the possibility to integrate interfaces for humans. On the downside, communication is necessary, resulting in the delayed reception of commands. In this article, we discuss the design considerations of the distributed approach, a sample implementation on a smartphone, and an application to the concrete use case of bookshelf inventory. Further, we report experimentally-derived first insights into messaging and command response delays with a custom drone connected through Wi-Fi.

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Dynamic Collaboration Between Networked Robots and Clouds in Resource-Constrained Environments

Cited by 39 publications

References 11 publications

The wave spectrum in archipelagos

The wave spectrum in archipelagos

Cloud-Based Multi-Agent Cooperation for IoT Devices Using Workflow-Nets

A Distributed Architecture for Human-Drone Teaming: Timing Challenges and Interaction Opportunities

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