Kinematic calibration of serial manipulators using Bayesian inference

SUMMARYIn this paper, a new calibration method for open-chain robotic arms is developed. By incorporating both prior parameter information and artifact measurement data, and by taking recourse to Bayesian inference methods, not only are the robot kinematic parameters updated but also confidence bounds are computed for all measurement data. In other words, for future measurement data not only the most likely end-effector configuration is estimated but also the uncertainty represented as 95% confidence bounds of that pose is computed. To validate the proposed calibration method, a three degree-of-freedom robotic arm was designed, constructed, and calibrated using both typical regression methods and the proposed calibration method. The results of an extensive set of experiments are presented to gauge the accuracy and utility of the proposed calibration method.

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Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints

Sensors ◽

10.3390/s21082673 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2673

Author(s):

Weibo Huang ◽

Weiwei Wan ◽

Hong Liu

Keyword(s):

Measurement Data ◽

Calibration Method ◽

Bundle Adjustment ◽

Measurement Unit ◽

Temporal Parameters ◽

Work Routine ◽

Initial States ◽

Time Offset ◽

Public Datasets ◽

Metric Scale

The online system state initialization and simultaneous spatial-temporal calibration are critical for monocular Visual-Inertial Odometry (VIO) since these parameters are either not well provided or even unknown. Although impressive performance has been achieved, most of the existing methods are designed for filter-based VIOs. For the optimization-based VIOs, there is not much online spatial-temporal calibration method in the literature. In this paper, we propose an optimization-based online initialization and spatial-temporal calibration method for VIO. The method does not need any prior knowledge about spatial and temporal configurations. It estimates the initial states of metric-scale, velocity, gravity, Inertial Measurement Unit (IMU) biases, and calibrates the coordinate transformation and time offsets between the camera and IMU sensors. The work routine of the method is as follows. First, it uses a time offset model and two short-term motion interpolation algorithms to align and interpolate the camera and IMU measurement data. Then, the aligned and interpolated results are sent to an incremental estimator to estimate the initial states and the spatial–temporal parameters. After that, a bundle adjustment is additionally included to improve the accuracy of the estimated results. Experiments using both synthetic and public datasets are performed to examine the performance of the proposed method. The results show that both the initial states and the spatial-temporal parameters can be well estimated. The method outperforms other contemporary methods used for comparison.

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An Introduction to Bayesian Inference, Methods and Computation

10.1007/978-3-030-82808-0 ◽

2021 ◽

Author(s):

Nick Heard

Keyword(s):

Bayesian Inference ◽

Inference Methods

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Xylodon bambusinus and X. xinpingensis spp. nov. (Hymenochaetales) from southern China

Phytotaxa ◽

10.11646/phytotaxa.511.3.3 ◽

2021 ◽

Vol 511 (3) ◽

Author(s):

XIANG MA ◽

CHANG-LIN ZHAO

Keyword(s):

New Species ◽

Bayesian Inference ◽

Maximum Likelihood ◽

Growth Habit ◽

Southern China ◽

Phylogenetic Analyses ◽

Molecular Data ◽

Two New Species ◽

Thin Walled ◽

Inference Methods

Two new species, Xylodon bambusinus and X. xinpingensis, are proposed based on morphological and molecular evidences. Both species share the annual growth habit, resupinate basidiomata and monomitic hyphal system with clamped, colorless generative hyphae, smooth, thin-walled basidiospores, but X. bambusinus is characterized by the smooth to tuberculate hymenial surface, presence of capitate and fusiform cystidia, broad ellipsoid basidiospores, while X. xinpingensis by the reticulate hymenophore with cream hymenial surface, and subglobose basidiospores (4.5–6 × 3.5–5 µm). Sequences of ITS and LSU nrRNA gene regions of the studied samples were generated, and phylogenetic analyses were performed with maximum likelihood, maximum parsimony and Bayesian inference methods. The phylogenetic analyses based on molecular data of ITS and ITS+nLSU sequences showed that X. bambusinus was sister to X. subclavatus, while X. xinpingensis grouped with X. astrocystidiatus and X. paradoxus. The nLSU dataset revealed that X. bambusinus grouped with X. asperus and X. brevisetus with lower supports, and that X. xinpingensis grouped with X. astrocystidiatus and X. paradoxus and then with X. rimosissimus without supports. Both morphological and molecular evidences confirmed the placement of two new species in Xylodon. Description and figures from the new species and a key to the known species of Xylodon from China are presented.

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Bayesian inference methods to calibrate crowd dynamics models for safety applications

Safety Science ◽

10.1016/j.ssci.2021.105586 ◽

2022 ◽

Vol 147 ◽

pp. 105586

Author(s):

Marion Gödel ◽

Nikolai Bode ◽

Gerta Köster ◽

Hans-Joachim Bungartz

Keyword(s):

Bayesian Inference ◽

Crowd Dynamics ◽

Inference Methods ◽

Dynamics Models ◽

Safety Applications

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Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK

PLoS ONE ◽

10.1371/journal.pone.0258968 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0258968

Author(s):

Patrick Pietzonka ◽

Erik Brorson ◽

William Bankes ◽

Michael E. Cates ◽

Robert L. Jack ◽

...

Keyword(s):

Bayesian Inference ◽

Time Window ◽

Fold Increase ◽

Strong Increase ◽

Mortality Data ◽

New Variant ◽

Model Evidence ◽

Age Structured ◽

The Uk ◽

Inference Methods

We apply Bayesian inference methods to a suite of distinct compartmental models of generalised SEIR type, in which diagnosis and quarantine are included via extra compartments. We investigate the evidence for a change in lethality of COVID-19 in late autumn 2020 in the UK, using age-structured, weekly national aggregate data for cases and mortalities. Models that allow a (step-like or graded) change in infection fatality rate (IFR) have consistently higher model evidence than those without. Moreover, they all infer a close to two-fold increase in IFR. This value lies well above most previously available estimates. However, the same models consistently infer that, most probably, the increase in IFR preceded the time window during which variant B.1.1.7 (alpha) became the dominant strain in the UK. Therefore, according to our models, the caseload and mortality data do not offer unequivocal evidence for higher lethality of a new variant. We compare these results for the UK with similar models for Germany and France, which also show increases in inferred IFR during the same period, despite the even later arrival of new variants in those countries. We argue that while the new variant(s) may be one contributing cause of a large increase in IFR in the UK in autumn 2020, other factors, such as seasonality, or pressure on health services, are likely to also have contributed.

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Calibration Method for a Parallel Mechanism Type Machine Tool by Response Surface Methodology –Consideration via Simulation on a Stewart Platform Mechanism–

International Journal of Automation Technology ◽

10.20965/ijat.2010.p0355 ◽

2010 ◽

Vol 4 (4) ◽

pp. 355-363 ◽

Cited By ~ 3

Author(s):

Hiroshi Yachi ◽

◽

Hiroshi Tachiya

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Parallel Mechanism ◽

Measurement Data ◽

Calibration Method ◽

Stewart Platform ◽

Parallel Mechanisms ◽

Data Set ◽

Efficient Data ◽

Efficient Measurement

This paper proposes a calibration method for parallel mechanisms usingResponse Surface Methodology. This method is a statistical approach to estimating an unknown input-output relationship using a small set of efficient data collected on an intended system. Although identifying locations causing positional errors in a parallel mechanism and precisely measuring the position and posture of the output point are difficult, the proposed calibration method based onResponse Surface Methodologyaims to compensate for positional and postural errors, without indentifying the locations causing these errors, by using a small yet efficient measurement data set. This study analyzes the effectiveness of the method we propose by applying it to a Stewart platform, which is a typical spatial 6-DOF parallel mechanism.

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Patternising Students’ performance in post graduate examinations using bayesian inference methods – a case study

The International Conference on "Information Science & Technology For Sustainability & Innovation" ◽

10.3850/978-981-09-4426-1_041 ◽

2015 ◽

Author(s):

Y. N. Ananth ◽

N. S. Narahari

Keyword(s):

Bayesian Inference ◽

Post Graduate ◽

Inference Methods

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Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory

Entropy ◽

10.3390/e22080890 ◽

2020 ◽

Vol 22 (8) ◽

pp. 890

Author(s):

Sergey Oladyshkin ◽

Farid Mohammadi ◽

Ilja Kroeker ◽

Wolfgang Nowak

Keyword(s):

Bayesian Inference ◽

Active Learning ◽

Gaussian Process ◽

Relative Entropy ◽

Bayesian Model ◽

Information Gain ◽

Measurement Data ◽

Superior Performance ◽

Evidence Based ◽

Model Evidence

Gaussian process emulators (GPE) are a machine learning approach that replicates computational demanding models using training runs of that model. Constructing such a surrogate is very challenging and, in the context of Bayesian inference, the training runs should be well invested. The current paper offers a fully Bayesian view on GPEs for Bayesian inference accompanied by Bayesian active learning (BAL). We introduce three BAL strategies that adaptively identify training sets for the GPE using information-theoretic arguments. The first strategy relies on Bayesian model evidence that indicates the GPE’s quality of matching the measurement data, the second strategy is based on relative entropy that indicates the relative information gain for the GPE, and the third is founded on information entropy that indicates the missing information in the GPE. We illustrate the performance of our three strategies using analytical- and carbon-dioxide benchmarks. The paper shows evidence of convergence against a reference solution and demonstrates quantification of post-calibration uncertainty by comparing the introduced three strategies. We conclude that Bayesian model evidence-based and relative entropy-based strategies outperform the entropy-based strategy because the latter can be misleading during the BAL. The relative entropy-based strategy demonstrates superior performance to the Bayesian model evidence-based strategy.

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Gloeodontia yunnanensis sp. nov. (Russulales, Basidiomycota) from China, evidenced by morphological characters and phylogenetic analyses

Phytotaxa ◽

10.11646/phytotaxa.432.2.1 ◽

2020 ◽

Vol 432 (2) ◽

pp. 111-118

Author(s):

LU CHEN ◽

ZHENG-JUN SHI ◽

CHUN-HUA WU ◽

CHANG-LIN ZHAO

Keyword(s):

Bayesian Inference ◽

Maximum Likelihood ◽

Maximum Parsimony ◽

Phylogenetic Analyses ◽

Strong Support ◽

Morphological Characters ◽

Fungal Species ◽

Monophyletic Lineage ◽

Thin Walled ◽

Inference Methods

A new wood-inhabiting fungal species, Gloeodontia yunnanensis, is proposed based on a combination of morphological features and DNA data. The species is characterized by an annual, resupinate basidiomata with smooth hymenial surface, a monomitic hyphal system with thin-walled, clamped generative hyphae and obclavate cystidia and subglobose to globose, hyaline, thick-walled, asperulate, strongly amyloid, acyanophilous basidiospores measuring 3.3–4.3 × 2.5–3.5 µm. Sequences of ITS and 28S gene regions of the studied samples were generated and phylogenetic analyses were performed with Maximum Likelihood, Maximum Parsimony and Bayesian Inference methods. The analyses based on ITS+28S sequences showed that G. yunnanensis nested in the Gloeodontia clade and formed a monophyletic lineage with strong support (100% BS, 100% BP, 1.00 BPP).

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