Coupled Controls-Computational Fluids Approach for the Estimation of the Concentration From a Moving Gaseous Source in a 2-D Domain With a Lyapunov-Guided Sensing Aerial Vehicle

Demetriou, Michael A.; Gatsonis, Nikolaos A.; Court, Jeffrey R.

doi:10.1109/tcst.2013.2267623

Cited by 53 publications

(26 citation statements)

References 65 publications

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“…Note that the field F is assumed to take strictly positive values everywhere in R 2 at any instant of time. In the context of UAV guidance, the field F may represent, for instance, terrain elevation [8,9,37], a strategic risk measure [19], a probabilistic occupancy grid [15], or the atmospheric concentration of a contaminant gas [38]. We make the following assumptions in the problem formulation.…”

Section: Ii-a Threat Fieldmentioning

confidence: 99%

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Multiresolution Aircraft Guidance in a Spatiotemporally-varying Threat Field

Cowlagi

2015

AIAA Guidance, Navigation, and Control Conference

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We consider the problem of generating an optimal aircraft reference trajectory in a horizontal plane, where the objective is to minimize exposure to a spatiotemporally varying scalar field. We consider a special case, where this field is timeinvariant, and we discuss optimal trajectory generation for an aircraft kinematic model based on first-and second-order necessary conditions of optimal control. Next, we consider the general case, with the additional assumption that the knowledge of this field is available in vehicle-centric multiresolution detail. For this general problem, we propose a hybrid algorithm that combines (continuous) trajectory optimization with a (discrete) geometric path-planning method. This path-planning method uses wavelet-based multiresolution cell decompositions to find a path to the destination through regions where the field intensity is known with lesser detail. The results of this geometric path-planning algorithm provide a terminal penalty function for the trajectory optimization problem. We provide illustrative numerical simulation results for the proposed algorithm.

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Section: Ii-a Threat Fieldmentioning

confidence: 99%

“…Assumptions 1 and 2 are conditions on the smoothness of F . Time-varying spatial fields arising from physical processes, such as advection and diffusion [38], are expected to satisfy this assumption. Assumptions 3 and 4 are for simplicity.…”

Section: Ii-a Threat Fieldmentioning

confidence: 99%

Multiresolution Aircraft Guidance in a Spatiotemporally-varying Threat Field

Cowlagi

2015

AIAA Guidance, Navigation, and Control Conference

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“…On other approaches in similar problem settings, we mention [4], [5] that also uses a state-estimator framework based on advection-diffusion equations, to estimate concentration from a moving gaseous source. The motion of the single aerial sensor is given by a Lyapunov-based control law improving the performance of a Luenberger state estimator.…”

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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“…of concentration of gas dispersion over a 2-dimensional (2-D) field by a modified Luenberger-type observer [13]. Burns and Rautenberg proposed an infinite-dimensional optimal filter for distributed parameter systems to compute optimal placements and trajectories of stationary and mobile sensors [14].…”

Section: Introductionmentioning

confidence: 99%

Distributed Source Identification by Two-Hop Consensus Dynamics with Uniform Time-Varying Communication Time-Delays

Hayashi

Takai

2017

SICE Journal of Control, Measurement, and System Integration

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: This paper considers the average consensus problem by the 2-hop communication with time-varying timedelays. Each agent updates its state by a discrete-time consensus dynamics with fixed and undirected communication networks to achieve the average consensus. We also consider an application of the proposed consensus dynamics to an inverse problem of diffusion phenomena in order to find a source of a diffusion process from time-series data measured by agents. The inverse problem of diffusion phenomena can be formulated as a linear least squares problem by the difference approximation. We show that agents can estimate the initial distribution of concentration by solving the least squares problem with the proposed consensus dynamics.

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Coupled Controls-Computational Fluids Approach for the Estimation of the Concentration From a Moving Gaseous Source in a 2-D Domain With a Lyapunov-Guided Sensing Aerial Vehicle

Cited by 53 publications

References 65 publications

Multiresolution Aircraft Guidance in a Spatiotemporally-varying Threat Field

Multiresolution Aircraft Guidance in a Spatiotemporally-varying Threat Field

Partially distributed optimization for mobile sensor path-planning

Distributed Source Identification by Two-Hop Consensus Dynamics with Uniform Time-Varying Communication Time-Delays

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