Path Tracking of a Wheeled Mobile Manipulator through Improved Localization and Calibration

This chapter focuses on path tracking of a wheeled mobile manipulator designed for manufacturing processes such as drilling, riveting, or line drawing, which demand high accuracy. This problem can be solved by combining two approaches: improved localization and improved calibration. In the first approach, a full-scale kinematic equation is derived for calibration of each individual wheel’s geometrical parameters, as opposed to traditionally treating them identical for all wheels. To avoid the singularity problem in computation, a predefined square path is used to quantify the errors used for calibration considering the movement in different directions. Both statistical method and interval analysis method are adopted and compared for estimation of the calibration parameters. In the second approach, a vision-based deviation rectification solution is presented to localize the system in the global frame through a number of artificial reflectors that are identified by an onboard laser scanner. An improved tracking and localization algorithm is developed to meet the high positional accuracy requirement, improve the system’s repeatability in the traditional trilateral algorithm, and solve the problem of pose loss in path following. The developed methods have been verified and implemented on the mobile manipulators developed by Shanghai University

Inverse Moment of the \u3cstrong\u3e\u3cem\u3eB\u3c/em\u3e\u3c/strong\u3e Meson Quasidistribution Amplitude

We perform a study on the structure of the inverse moment (IM) of quasidistributions, by taking B-meson quasidistribution amplitude (quasi-DA) as an example. Based on a one-loop calculation, we derive the renormalization group equation and velocity evolution equation for the first IM of quasi-DA. We find that, in the large velocity limit, the first IM of B-meson quasi-DA can be factorized into IM as well as logarithmic moments of light-cone distribution amplitude (LCDA), accompanied by short distance coefficients. Our results can be useful either in understanding the patterns of perturbative matching in large momentum effective theory or evaluating inverse moment of B-meson LCDA on the lattice

Association between the methylenetetrahydrofolate reductase C677T polymorphism and hepatocellular carcinoma risk: a meta-analysis

Abstract Background Methylenetetrahydrofolate reductase (MTHFR) is a key enzyme in the metabolism of folate. The non-synonymous single nucleotide polymorphism (nsSNP), C677T (Ala>Val, rs1801133), has been verified to impair enzyme activity. The association with cancer susceptibility, including hepatocellular carcinoma (HCC), has also been widely studied. The results, however, were inconsistent. To shed light on the influence of MTHFR C677T polymorphism on HCC, a meta-analysis was conducted. Methods The meta-analysis of C677T consisted of 10 studies (1814 cases/2862 controls). The association was measured by using random-effect (RE) or fixed-effect (FE) odds ratio (OR) combined with 95% confidence intervals (CIs) according to the studies' heterogeneity. Results Using genetic model analysis, C677T polymorphism was found to increase the risk of HCC in a complete overdominant model, which indicates that heterozygotes CT are at a lesser risk of HCC than either homozygotes CC or TT. Meta-analyses of the 10 studies showed that the TT genotype increased the risk of HCC as compared to the CT genotype: FE OR was 1.20 (95%CI: 1.00-1.45, p for heterogeneity = 0.21). When subgroup analysis was done between the HCC cases and the chronic liver disease (CLD) patients of four studies, meta-analysis showed that individuals with the TT genotype had increased HCC risk compared with those with the CT genotype: FE OR (TT vs. CT) reached 1.81 (1.22-2.71, p for heterogeneity = 0.25). Meanwhile, the C677T polymorphism also increased HCC risk in a recessive model when cases were compared to CLD patients of four studies: RE OR reached 1.85 (95%CI: 1.00-3.42, p for heterogeneity = 0.06). Overall, there was some extent heterogeneity when analyses were performed in various models. There was no publication bias. Conclusion MTHFR C677T polymorphism increased the risk of HCC in an overdominant model, and might be a risk factor for HCC occurrence, especially in CLD patients. The association warranted further studies.</p

The Role of Edge Robotics As-a-Service in Monitoring COVID-19 Infection

Deep learning technology has been widely used in edge computing. However, pandemics like covid-19 require deep learning capabilities at mobile devices (detect respiratory rate using mobile robotics or conduct CT scan using a mobile scanner), which are severely constrained by the limited storage and computation resources at the device level. To solve this problem, we propose a three-tier architecture, including robot layers, edge layers, and cloud layers. We adopt this architecture to design a non-contact respiratory monitoring system to break down respiratory rate calculation tasks. Experimental results of respiratory rate monitoring show that the proposed approach in this paper significantly outperforms other approaches. It is supported by computation time costs with 2.26 ms per frame, 27.48 ms per frame, 0.78 seconds for convolution operation, similarity calculation, processing one-minute length respiratory signals, respectively. And the computation time costs of our three-tier architecture are less than that of edge+cloud architecture and cloud architecture. Moreover, we use our three-tire architecture for CT image diagnosis task decomposition. The evaluation of a CT image dataset of COVID-19 proves that our three-tire architecture is useful for resolving tasks on deep learning networks by edge equipment. There are broad application scenarios in smart hospitals in the future