Robust Gaussian Filtering using a Pseudo Measurement

Many sensors, such as range, sonar, radar, GPS and visual devices, produce measurements which are contaminated by outliers. This problem can be addressed by using fat-tailed sensor models, which account for the possibility of outliers. Unfortunately, all estimation algorithms belonging to the family of Gaussian filters (such as the widely-used extended Kalman filter and unscented Kalman filter) are inherently incompatible with such fat-tailed sensor models. The contribution of this paper is to show that any Gaussian filter can be made compatible with fat-tailed sensor models by applying one simple change: Instead of filtering with the physical measurement, we propose to filter with a pseudo measurement obtained by applying a feature function to the physical measurement. We derive such a feature function which is optimal under some conditions. Simulation results show that the proposed method can effectively handle measurement outliers and allows for robust filtering in both linear and nonlinear systems

Experimental analysis of nanostructured PEEK, African giant snail shell, and sea snail shell powder for hydroxyapatite formation for bone implant applications

This experimental research focuses on the nanostructure analysis of three materials; polyether ether ketone (PEEK), African land giant snail shell (ALGSS), and sea snail shell (SSS) powder, for the formation of hydroxyapatite (HA) coatings in bone implant applications. The study aimed to evaluate these materials’ surface characteristics, furrow depth, density, and other relevant parameters to assess their suitability as bone implant materials. The nanostructure analysis revealed distinct characteristics for each material. PEEK exhibited shallow furrows and a high density of furrows, making it a favourable substrate for hydroxyapatite coating formation. The ISO 25178 roughness analysis further characterised surface roughness and topography. African land giant snail shell powder, displayed a high material ratio, indicating a potential for hydroxyapatite conversion for biomedical application. The sea snail shell powder demonstrated intermediate furrow depth and density, warranting further investigation for optimisation as a precursor for hydroxyapatite coatings. The findings emphasise the significance of nanostructure properties in bone implant materials. The tailored nanostructure of materials such as PEEK, the synthesized powder can influence their biocompatibility, osseointegration, and long-term performance. The novelty of this research lies in the comprehensive analysis of the nanostructure properties of these materials, contributing to the understanding of their potential for bone implant applications. Overall, this experimental research is significant and provides valuable insights into the nanostructure characteristics of PEEK, African land giant snail shell powder, and sea snail shell powder and they all demonstrated the potential of forming hydroxyapatite coatings.</p

Gaussian process machine learning-based surface extrapolation method for improvement of the edge effect in surface filtering

Filtering for signal and data is an important technology to reduce and/or remove noise signal for further extraction of desired information. However, it is well known that significant distortions may occur in the boundary areas of the filtered data because there is no sufficient data to be processed. This drawback largely affects the accuracy of topographic measurements and characterizations of precision freeform surfaces, such as freeform optics. To address this issue, a Gaussian process machine learning-based method is presented for extrapolation of the measured surface to an extended measurement area with high accuracy prior to filtering the surface. With the extrapolated data, the edge distortion can be effectively reduced. The effectiveness of this method was evaluated using both simulated and experimental data. Successful implementation of the proposed method not only addresses the issue in surface filtering but also provides a promising solution for numerous applications involving filtering processes

Construct Surface Characterization System by Assembling Functional Components Dynamically

Surface characterization of manufactured components is regarded as an important process to figure out surface features, which are closely related to the manufacture process and will affect their functionality. Due to the complicated computation, the actual operations are mostly completed by the aid of surface characterization software. Nowadays, these systems are mainly exploited by instrument companies and embedded in surface measurement instruments. Although it is convenient for users to evaluate surfaces straightforwardly after measurement, the results are usually incomparable with those from other surface instruments because of the different characterization systems. Moreover, the system evolution will cost too much due to the lack of flexibility and extendibility. This paper presents a component based architecture which facilitates the system construction by assembling functional components dynamically

A study of extrapolation of freeform surfaces to improve the edge effect in surface filtering

Surface filtering is a hot research topic especially in the field of freeform surface metrology, since filtering is an important data processing technique before further characterization of the measured surfaces. There is a large number of surface filtering algorithms developed by researchers to improve the robustness and accuracy of the filtering results. However, the filtering result is still far from complete which is particularly true in the edge area where is always found to have large distortion. This is so-called the edge effect which is mainly caused by a lack of data when performing convolution in the edge area in the filtering algorithms. In this paper, a Gaussian process machine learning-based surface extrapolation method of the measurement data is presented to extend the measured surface before conducting surface filtering. A Gaussian process data modelling method is utilized for the surface extrapolation and hence a Gaussian filtering method is used for the surface filtering. A series of simulation and practical measurement experiments have been conducted to evaluate the performance of the proposed method. The accuracy and efficiency of the new filtering method are demonstrated and analyzed in the experiments. The results show that the edge effect can be significantly improved and the efficiency can also be improved by introducing the extrapolation method. The proposed method provides a new way for surface filtering and thus surface characterization for the complex freeform surfaces

Push to know! -- Visuo-Tactile based Active Object Parameter Inference with Dual Differentiable Filtering

For robotic systems to interact with objects in dynamic environments, it is essential to perceive the physical properties of the objects such as shape, friction coefficient, mass, center of mass, and inertia. This not only eases selecting manipulation action but also ensures the task is performed as desired. However, estimating the physical properties of especially novel objects is a challenging problem, using either vision or tactile sensing. In this work, we propose a novel framework to estimate key object parameters using non-prehensile manipulation using vision and tactile sensing. Our proposed active dual differentiable filtering (ADDF) approach as part of our framework learns the object-robot interaction during non-prehensile object push to infer the object's parameters. Our proposed method enables the robotic system to employ vision and tactile information to interactively explore a novel object via non-prehensile object push. The novel proposed N-step active formulation within the differentiable filtering facilitates efficient learning of the object-robot interaction model and during inference by selecting the next best exploratory push actions (where to push? and how to push?). We extensively evaluated our framework in simulation and real-robotic scenarios, yielding superior performance to the state-of-the-art baseline.Comment: 8 pages. Accepted at IROS 202