Multi-objective optimization of dead-reckoning error thresholds for virtual environments

Millar, Jeremy R.; Hodson, Douglas D.; Lamont, Gary B.; Peterson, Gilbert L.

doi:10.1109/cts.2014.6867626

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…But the positioning error based on DR information grows without bound. In the range of a few hundred meters from the sea floor, the positioning error is generally 0.5%− 2% of the mileage, so the DVL will be locked when working under the sea [14]. Errors as low as 0.1% can be obtained with large and expensive INS systems, however, the cost will be enormous if each AUV is equipped with high-precision INS.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Positioning for Multi-AUVs Based on Factor Graph and Maximum Correntropy

et al. 2019

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Cooperative positioning (CP) is considered as a promising positioning method for multiple autonomous underwater vehicles (multi-AUVs), because CP is characterized by low cost and high precision. In this research, a novel autonomous underwater vehicle (AUV) CP algorithm is proposed to enhance the global localization accuracy of the follower AUV. However, in traditional CP algorithm, the positioning error is large under the condition that the outlier data exists in the observation, which happens commonly. So in this research, a novel CP algorithm based on the factor graph and maximum correntropy(FGMC) for AUV is proposed to enhance the global localization accuracy of the AUV. Different from the traditional algorithms, this presented FGMC-based CP algorithm implements mathematically the Bayes filter by converting the global function estimation problem into the local one. And furthermore, the maximum correntropy is used as the cost function in the factor graph to estimation problem, this can reduce the influence of outliers on positioning accuracy. FGMC based cooperative positioning algorithm is established to mathematically implement the Bayes filter by converting the global function estimation problem into local function estimation problem. Furthermore, the maximum correntropy is used as the cost function in the factor graph to estimate the variables. To demonstrate and verify the proposed algorithm, simulation and real tests in different scenarios are performed in this research. Compared with the traditional CP algorithms, the positioning error of the proposed FGMC cooperative positioning algorithm is obviously smaller than that of the other algorithms.

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Section: Introductionmentioning

confidence: 99%

Cooperative Positioning for Multi-AUVs Based on Factor Graph and Maximum Correntropy

et al. 2019

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“…Here, fairness is defined in a cluster cohesion metric that ensures all simulation users see similar levels of state consistency and response time. This work was originally presented in Millar et al 13 The second algorithm extends the previous work to virtual environments using periodic state updates and is based on the notion of interaction contexts 14 and plausibility limits. We introduce the notion of plausibility limits as the maximum tolerable state inconsistency that allows all participants in an interaction to reach the same conclusion or outcome.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing update scheduling parameters for distributed virtual environments supporting operational test

Millar

Hodson

Peterson

et al. 2017

Concurrency and Computation

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Distributed virtual environments provide a shared sense of time and space to geographically distributed users. They depend on a limited ability to ensure system nodes at different locations maintain consistent state data. This paper presents system models and algorithms designed to find optimal update scheduling parameters that minimize the effects of inconsistent simulation state data. The first model is concerned with ensuring distributed virtual environments that present a fair experience to all users while simultaneously providing adequate levels of system performance. In the second model, we introduce the concept of plausibility limits and address their use in ensuring all participants in an interaction to see the same result.

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An Advanced Cooperative Positioning Algorithm Based on Improved Factor Graph and Sum-Product Theory for Multiple AUVs

Zhang

Zhu

et al. 2019

IEEE Access

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Multi-objective optimization of dead-reckoning error thresholds for virtual environments

Cited by 3 publications

References 26 publications

Cooperative Positioning for Multi-AUVs Based on Factor Graph and Maximum Correntropy

Cooperative Positioning for Multi-AUVs Based on Factor Graph and Maximum Correntropy

Optimizing update scheduling parameters for distributed virtual environments supporting operational test

An Advanced Cooperative Positioning Algorithm Based on Improved Factor Graph and Sum-Product Theory for Multiple AUVs

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