Model-based sparse source identification

Khodayi-mehr, Reza; Aquino, Wilkins; Zavlanos, Michael M.

doi:10.1109/acc.2015.7170997

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…The proposed state estimation framework, described in Algorithm 5, can be applied to any state estimation problem, such as target tracking [12], estimation of spatio-temporal fields [27], or state estimation in distributed parameter systems [56]- [58], as long as the cost function Cost(P Ti ) is additive and monotone. In this section, we demonstrate the performance of the proposed algorithm for a target tracking problem in a non-convex environment.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Distributed State Estimation Using Intermittently Connected Robot Networks

2019

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This paper considers the problem of distributed state estimation using multi-robot systems. The robots have limited communication capabilities and, therefore, communicate their measurements intermittently only when they are physically close to each other. To decrease the distance that the robots need to travel only to communicate, we divide them into small teams that can communicate at different locations to share information and update their beliefs. Then, we propose a new distributed scheme that combines (i) communication schedules that ensure that the network is intermittently connected, and (ii) samplingbased motion planning for the robots in every team with the objective to collect optimal measurements and decide a location for those robots to communicate. To the best of our knowledge, this is the first distributed state estimation framework that relaxes all network connectivity assumptions, and controls intermittent communication events so that the estimation uncertainty is minimized. We present simulation results that demonstrate significant improvement in estimation accuracy compared to methods that maintain an end-to-end connected network for all time.Index Terms-Distributed state estimation, intermittent connectivity, sampling-based planning, multi-robot networks.

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Section: Numerical Experimentsmentioning

confidence: 99%

Distributed State Estimation Using Intermittently Connected Robot Networks

2019

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“…The authors in [17] use the FEM along with total variation regularization to solve the SI problem. Similarly, in our previous work [18], we proposed the Reweighted Debiased 1 algorithm, which is an iterative sparse recovery approach to the SI problem. Despite generality, numerical methods such as FEM become computationally demanding as the size of the domain grows.…”

Section: A the Source Identification Problemmentioning

confidence: 99%

“…Thus c d (x; p) = ψ(x) A −1 b(p). Therefore, we can calculate the desired derivative in the definition of the FIM (18) as…”

Section: Mobile Robot Path Planningmentioning

confidence: 99%

Model-Based Active Source Identification in Complex Environments

2019

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We consider the problem of Active Source Identification (ASI) in steady-state Advection-Diffusion (AD) transport systems. Unlike existing bio-inspired heuristic methods, we propose a model-based method that employs the AD-PDE to capture the transport phenomenon. Specifically, we formulate the Source Identification (SI) problem as a PDE-constrained optimization problem in function spaces. To obtain a tractable solution, we reduce the dimension of the concentration field using Proper Orthogonal Decomposition and approximate the unknown source field using nonlinear basis functions, drastically decreasing the number of unknowns. Moreover, to collect the concentration measurements, we control a robot sensor through a sequence of waypoints that maximize the smallest eigenvalue of the Fisher Information matrix of the unknown source parameters. Specifically, after every new measurement, a SI problem is solved to obtain a source estimate that is used to determine the next waypoint. We show that our algorithm can efficiently identify sources in complex AD systems and non-convex domains, in simulation and experimentally. This is the first time that PDEs are used for robotic SI in practice.

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“…The MTC problem is closely related to disturbance control of DPSs [18] and Source Identification (SI) [19]- [21]. The objective in the former is to maintain the equilibrium of a DPS against an exogenous disturbance moving in the domain whereas in the latter it is to identify a source function and a corresponding concentration field that matches an observed set of concentrations.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Robotic Mass Transport Cloaking

Khodayi-mehr

Zavlanos

2020

IEEE Trans. Robot.

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We consider the problem of Mass Transport Cloaking using mobile robots. The robots move along a predefined curve that encloses the safe zone and carry sources that collectively counteract a chemical agent released in the environment. The goal is to steer the mass flux around a desired region so that it remains unaffected by the external concentration. We formulate the problem of controlling the robot positions and release rates as a PDE-constrained optimization, where the propagation of the chemical is modeled by the Advection-Diffusion (AD) PDE. We use a Deep Neural Network (NN) to approximate the solution of the PDE. Particularly, we propose a novel loss function for the NN that utilizes the variational form of the AD-PDE and allows us to reformulate the planning problem as an unsupervised modelbased learning problem. Our loss function is discretization-free and highly parallelizable. Unlike passive cloaking methods that use metamaterials to steer the mass flux, our method is the first to use mobile robots to actively control the concentration levels and create safe zones independent of environmental conditions. We demonstrate the performance of our method in simulations.

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Model-based sparse source identification

Cited by 10 publications

References 16 publications

Distributed State Estimation Using Intermittently Connected Robot Networks

Distributed State Estimation Using Intermittently Connected Robot Networks

Model-Based Active Source Identification in Complex Environments

Deep Learning for Robotic Mass Transport Cloaking

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