Probability map building of uncertain dynamic environments with indistinguishable obstacles

Murai, Jun; D’Andrea, Raffaello

doi:10.1109/acc.2003.1244060

Cited by 14 publications

(14 citation statements)

References 12 publications

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“…[29], Jun and D'Andrea [21] and [34], and McManus et al [38]. In this research, a pre-mission planner provides a schedule, and after the mission starts, the plan can be adjusted with the online/onboard application if it is not followed as scheduled.…”

Section: Contributionsmentioning

confidence: 99%

“…Targets were assigned to UAVs and divided equally among them. [21] and [34]. Both moving and fixed threats were used in Schouwenaars et al [20].…”

Section: Overviewmentioning

confidence: 99%

“…The model also considered the UAV speed and minimum turn radius restrictions. Jun and D'Andrea [21] built an online probabilistic map algorithm based on Bayesian estimation for an uncertain dynamic environment of multiple moving obstacles. The probability distribution function was updated based on the limited data measurements received from sensors as the vehicle moves.…”

Section: Probabilistic Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Unmanned aerial vehicle routing in the presence of threats

Alotaibi

Rosenberger

Mattingly

et al. 2018

Computers & Industrial Engineering

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Section: Contributionsmentioning

confidence: 99%

“…Targets were assigned to UAVs and divided equally among them. [21] and [34]. Both moving and fixed threats were used in Schouwenaars et al [20].…”

Section: Overviewmentioning

confidence: 99%

Section: Probabilistic Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Unmanned aerial vehicle routing in the presence of threats

Alotaibi

Rosenberger

Mattingly

et al. 2018

Computers & Industrial Engineering

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“…The target information can, thus, be described using a probability or belief map [10], [11], [12], [13], [14], [15] whose entries are the probabilities for finding targets in the corresponding cell. In other words, the belief map is a gridded representation of the environment.…”

Section: Introductionmentioning

confidence: 99%

Multiple target detection using Bayesian learning

Nair

Chevva²,

Owhadi

et al. 2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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Abstract-In this paper, we study multiple target detection using Bayesian learning. The main aim of the paper is to present a computationally efficient way to compute the belief map update exactly and efficiently using results from the theory of symmetric polynomials. In order to illustrate the idea, we consider a simple search scenario with multiple search agents and an unknown but fixed number of stationary targets in a given region that is divided into cells. To estimate the number of targets, a belief map for number of targets is also propagated. The belief map is updated using Bayes' theorem and an optimal reassignment of vehicles based on the values of the current belief map is adopted. Exact computation of the belief map update is combinatorial in nature and often an approximation is needed. We show that the Bayesian update can be exactly computed in an efficient manner using Newton's identities and the detection history in each cell.

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“…[1], [2], [3], [5], [9], [11], [12] and the references therein). The quality and timeliness of this information will have a direct impact on the overall mission performance.…”

Section: Introductionmentioning

confidence: 99%

Robust UAV Search for Environments with Imprecise Probability Maps

Bertuccelli

How

Proceedings of the 44th IEEE Conference on Decision and Control

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This paper introduces a new framework for UAV search operations and proposes a new approach to calculate the minimum number of looks needed to achieve a given level of confidence of target existence in an uncertain gridded environment. Typical search theory formulations describe the uncertainty in the environment in a probabilistic fashion, by assigning probabilities of target existence to the individual cells of the grid. While assumed to be precisely known in the search theory literature, these probabilities are often the result of prior information and intelligence, and will likely be poorly known. The approach taken in this paper models this imprecise knowledge of the prior probabilities in the individual cells using the Beta distribution and generates search actions that are robust to the uncertainty. Use of the Beta distribution leads to an analytical prediction of the number of looks in a particular cell that would be needed to achieve a specified threshold in the confidence of target existence. The analytical results are demonstrated in both an expected value setting and a framework that takes into account the variance of the posterior distribution. The effectiveness of the proposed framework is demonstrated in several numerical simulations.

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Probability map building of uncertain dynamic environments with indistinguishable obstacles

Cited by 14 publications

References 12 publications

Unmanned aerial vehicle routing in the presence of threats

Unmanned aerial vehicle routing in the presence of threats

Multiple target detection using Bayesian learning

Robust UAV Search for Environments with Imprecise Probability Maps

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