Autonomous Localization of an Unknown Number of Targets Without Data Association Using Teams of Mobile Sensors

Dames, Philip; Kumar, Vijay

doi:10.1109/tase.2015.2425212

Cited by 72 publications

(49 citation statements)

References 26 publications

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“…In addition, it allows tracking multiple moving targets without added complexity as opposed to most IM techniques that would need to plan a motion for each target separately. To overcome this issue with IM techniques, Dames et al [58], [59] propose an estimation filter that estimates the targets' density-instead of individual labeled targets-over time, thus rendering IM complexity independent of the number of targets. However, the proposed trajectory generation methodology relies on exhaustive search requiring discretization of the controls space.…”

Section: B Motion Planning For Target Localizationmentioning

confidence: 99%

Real-Time Area Coverage and Target Localization Using Receding-Horizon Ergodic Exploration

et al. 2018

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Abstract-Although a number of solutions exist for the problems of coverage, search and target localization-commonly addressed separately-whether there exists a unified strategy that addresses these objectives in a coherent manner without being application-specific remains a largely open research question. In this paper, we develop a receding-horizon ergodic control approach, based on hybrid systems theory, that has the potential to fill this gap. The nonlinear model predictive control algorithm plans real-time motions that optimally improve ergodicity with respect to a distribution defined by the expected information density across the sensing domain. We establish a theoretical framework for global stability guarantees with respect to a distribution. Moreover, the approach is distributable across multiple agents, so that each agent can independently compute its own control while sharing statistics of its coverage across a communication network. We demonstrate the method in both simulation and in experiment in the context of target localization, illustrating that the algorithm is independent of the number of targets being tracked and can be run in real-time on computationally limited hardware platforms.

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Section: B Motion Planning For Target Localizationmentioning

confidence: 99%

Real-Time Area Coverage and Target Localization Using Receding-Horizon Ergodic Exploration

et al. 2018

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“…Proof of ineq. (14): Consider that the objective function J is non-decreasing in the active robot set, such that (without loss of generality) J is non-negative and J[u 1:T (∅)] = 0. Similarly with the observations we made in the proof of ineq.…”

Section: Proof Of Lemma 3: Letmentioning

confidence: 99%

Resilient Active Information Gathering with Mobile Robots

Schlotfeldt

Tzoumas

Thakur

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Applications in robotics, such as multi-robot target tracking, involve the execution of information acquisition tasks by teams of mobile robots. However, in failure-prone or adversarial environments, robots get attacked, their communication channels get jammed, and their sensors fail, resulting in the withdrawal of robots from the collective task, and, subsequently, the inability of the remaining active robots to coordinate with each other. As a result, traditional design paradigms become insufficient and, in contrast, resilient designs against system-wide failures and attacks become important. In general, resilient design problems are hard, and even though they often involve objective functions that are monotone and (possibly) submodular, scalable approximation algorithms for their solution have been hitherto unknown. In this paper, we provide the first algorithm, enabling the following capabilities: minimal communication, i.e., the algorithm is executed by the robots based only on minimal communication between them; system-wide resiliency, i.e., the algorithm is valid for any number of denial-of-service attacks and failures; and provable approximation performance, i.e., the algorithm ensures for all monotone and (possibly) submodular objective functions a solution that is finitely close to the optimal. We support our theoretical analyses with simulated and realworld experiments, by considering an active information acquisition application scenario, namely, multi-robot target tracking. ).This research is partially supported by ARL CRA DCIST W911NF-17-2-0181 and the Rockefeller Foundation.• (Problem definition) We formalize the problem of resilient active information gathering with mobile robots against multi-robot denial-of-service attacks or failures. arXiv:1803.09730v3 [cs.RO] 2 Sep 2018 APPENDIX A PRELIMINARY LEMMAS AND DEFINITIONSNotation. In the appendix we use the following notation to support the proofs in this paper: in particular, consider a finite ground set V and a set function f : 2 V → R. Then, for any set X ⊆ V and any set X ⊆ V, the symbol f (X |X ) denotes the marginal value f (X ∪ X ) − f (X ). Moreover, the symbol κ f is the total curvature of f (Definition 3), and the symbol c f is the total curvature of f (Definition 4).This appendix contains lemmas that will support the proof of Theorem 1 in this paper; moreover, it contains a generalized description of the algorithm coordinate descent [20, Section IV] (to any non-decreasing information objective function in the active robot set), and a lemma, which will support the proof of Proposition 1 in this paper.

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“…More recently, [16] used the PHD filter to implement an active strategy to detect and register target locations.…”

Section: Paul Reverdy and Daniel E Koditschekmentioning

confidence: 99%

Mobile robots as remote sensors for spatial point process models

Reverdy

Koditschek

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Spatial point process models are a commonly-used statistical tool for studying the distribution of objects of interest in a domain. We study the problem of deploying mobile robots as remote sensors to estimate the parameters of such a model, in particular the intensity parameter lambda which measures the mean density of points in a Poisson point process. This problem requires covering an appropriately large section of the domain while avoiding the objects, which we treat as obstacles. We develop a control law that covers an expanding section of the domain and an online criterion for determining when to stop sampling, i.e., when the covered area is large enough to achieve a desired level of estimation accuracy, and illustrate the resulting system with numerical simulations. KeywordsSpatial point processes, remote sensing, mobile robots, coverage, stopping rule Disciplines Electrical and Computer Engineering | Engineering | Systems Engineering Comments missing image.gifThis conference paper is available at ScholarlyCommons: http://repository.upenn.edu/ese_papers/716Mobile robots as remote sensors for spatial point process models Paul Reverdy and Daniel E. KoditschekAbstract-Spatial point process models are a commonly-used statistical tool for studying the distribution of objects of interest in a domain. We study the problem of deploying mobile robots as remote sensors to estimate the parameters of such a model, in particular the intensity parameter λ which measures the mean density of points in a Poisson point process. This problem requires covering an appropriately large section of the domain while avoiding the objects, which we treat as obstacles. We develop a control law that covers an expanding section of the domain and an online criterion for determining when to stop sampling, i.e., when the covered area is large enough to achieve a desired level of estimation accuracy, and illustrate the resulting system with numerical simulations.Mobile robots have been extensively used as platforms for remote sensors in recent years. Much of the statisticallyrigorous research on such platforms has focused on sensing and mapping continuous fields using various models. This has led to a series of results that focus on coordinating a set of robotic vehicles The mobile sensor network literature has placed significantly less emphasis on spatial point process models, which are statistical models that capture the distribution of discrete objects in space. Point process models are the subject of extensive study in the statistical literature [7] and have proven useful in numerous applied fields. In ecology, point process models have been used to study the distribution of plants in a domain [8], [9], [10], while in search and rescue they have been used to study the distribution of distress calls in a service area [11], and in disaster management they have been used for modeling the distribution of forest fires [12].Consideration of spatial point process models has recently begun to appear in the robotics literature, albeit l...

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Autonomous Localization of an Unknown Number of Targets Without Data Association Using Teams of Mobile Sensors

Cited by 72 publications

References 26 publications

Real-Time Area Coverage and Target Localization Using Receding-Horizon Ergodic Exploration

Real-Time Area Coverage and Target Localization Using Receding-Horizon Ergodic Exploration

Resilient Active Information Gathering with Mobile Robots

Mobile robots as remote sensors for spatial point process models

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