Monitoring an Advection-Diffusion Process Using Aerial Mobile Sensors

Haugen, Joakim; Imsland, Lars

doi:10.1142/s2301385015500144

Cited by 14 publications

(9 citation statements)

References 29 publications

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“…The latter could be termed "(dynamic) coverage control", where dynamic allude to the drifting nature of sea-ice. Results towards iceberg surveillance, including experiments, can be found in Imsland, 2013a,b, 2014c), while approaches towards surveillance of ice drift can be found in (Haugen et al, 2012;Haugen and Imsland, 2014b). These forms the basis for Joakim Haugen's PhD thesis (Haugen, 2014), which is in part summarized in Haugen and Imsland (2014a).…”

Section: Aerial Ice Monitoring By Dronesmentioning

confidence: 97%

The Arctic DP Research Project: Effective Stationkeeping in Ice

Skjetne¹,

Imsland²,

Løset³

2014

MIC

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Stress on the environment from a potentially growing energy use is set to rise. Without doubt the energy resources in Arctic regions will be developed. An important goal will be to exploit the resources offered by for instance the Barents Sea as a new European energy province, and to do this in accordance with the principles of sustainable development that have successfully been used e.g. in the North Sea.The special edition of MIC on Arctic DP presents a set of articles that summarize to an extent the activities of the research project "Arctic DP: Safe and green dynamic positioning operations of offshore vessels in an Arctic environment". This project was awarded in 2010 by the Research Council of Norway (RCN) as a competence-building project (KMB project) to NTNU and its partners Kongsberg Maritime, DNV GL, and Statoil. The objective was to target some of the challenges related to safe Arctic offshore operations by dynamic positioning. In this first article of the Arctic DP special edition we discuss the background for and establishment of the project, its planning and execution, and project closure. An overview is given for the scientific and engineering research performed in the project, with an account of what we have considered as Effective stationkeeping in ice by dynamic positioning. The corresponding research activities conducted under this main theme is summarized.

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Section: Aerial Ice Monitoring By Dronesmentioning

confidence: 97%

The Arctic DP Research Project: Effective Stationkeeping in Ice

Skjetne¹,

Imsland²,

Løset³

2014

MIC

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“…In this manuscript, we briefly discuss the following ice observation sub-tasks: target tracking, related to Mission 1, and dynamic coverage control, related to Mission 2. We provide an overview of common components that have been used to solve the mentioned sub-tasks in Haugen and Imsland (2014b) and Haugen and Imsland (2014a), respectively. We will finally display results from a hybrid field experiment of the target tracking task, which was first reported in Haugen and Imsland (2013b).…”

Section: Remotely Piloted Aircraft Systems In Cold Regionsmentioning

confidence: 99%

“…Previous work on state estimation dynamic coverage falls under two main approaches: optimal control formulations (Burns et al, 2009;Choi and How, 2010;Haugen and Imsland, 2014a), and gradient-based guidance algorithms (Demetriou and Hussein, 2009;Demetriou, 2010;Haugen et al, 2012). In the optimal control problem (OCP) formulation, one seeks to minimize some kind of objective functions that quantify the information reward of visiting a particular region.…”

Section: Dynamic Coverage Controlmentioning

confidence: 99%

“…In the optimal control problem (OCP) formulation, one seeks to minimize some kind of objective functions that quantify the information reward of visiting a particular region. The formulations often use a measure based on the estimation error covariance dynamics (Burns et al, 2009;Choi and How, 2010), but to facilitate computational speed, simplified information dynamics has also been proposed (Haugen and Imsland, 2014a). Gradient-based guidance algorithms are more computationally efficient, but often rely on locally available estimation error to guide the vehicles (Demetriou and Hussein, 2009;Demetriou, 2010;Haugen et al, 2012).…”

Section: Dynamic Coverage Controlmentioning

confidence: 99%

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Autonomous Aerial Ice Observation for Ice Defense

Haugen¹,

Imsland²

2014

MIC

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One of the tasks in ice defense is to gather information about the surrounding ice environment using various sensor platforms. In this manuscript we identify two monitoring tasks known in literature, namely dynamic coverage and target tracking, and motivate how these tasks are relevant in ice defense using remotely piloted aircraft systems (RPASs). An optimization-based path planning concept is outlined for solving these tasks. A path planner for the target tracking problem is elaborated in more detail and a hybrid experiment, which consists of both a real fixed-wing aircraft and simulated objects, is included to show the applicability of the proposed framework.

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“…We also mention [3] that avoids the complexity of propagating the covariance matrix dynamics (the Riccati equations) in a receding horizon framework, by instead propagating an uncertainty measure using the advection-diffusion process directly. Motion planning is done by formulating a receding horizon optimization problem for possibly multiple sensors.…”

Section: Introductionmentioning

confidence: 99%

Partially distributed optimization for mobile sensor path-planning

Imsland

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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Abstract-Mobile sensor platforms may provide low-cost, versatile means for obtaining high resolution information from spatially distributed processes. The aim of this paper is to efficiently calculate receding horizon-type motion plans that optimize information retrival by mobile sensors from distributed processes. Formulating the problem in a rather general framework as minimization of entropy, gives a huge, nonconvex, in general intractable optimization problem for path planning. Based on this formulation, using approximation and decomposition strategies, we propose a new, more computationally tractable framework. In the case of multiple mobile sensors, the framework allows a partially distributed setup where each mobile sensor optimize its path using an economic-type Model Predictive Controller, where the "economic" (in the sense of information) objective is constructed based on simulation of covariances for the distributed process, performed by a central entity.

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Monitoring an Advection-Diffusion Process Using Aerial Mobile Sensors

Cited by 14 publications

References 29 publications

The Arctic DP Research Project: Effective Stationkeeping in Ice

The Arctic DP Research Project: Effective Stationkeeping in Ice

Autonomous Aerial Ice Observation for Ice Defense

Partially distributed optimization for mobile sensor path-planning

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