Analysis of the Snake Robot Kinematics with Virtual Reality Visualisation.

In this article, we present a novel approach to performing engineering simulation in an interactive environment. A synesthetic design approach is employed, which enables the user to gather information about the system's behaviour more holistically, at the same time as facilitating interaction with the simulated system. The system considered in this work is a snake robot moving on a flat surface. The dynamic simulation of the robot's movement is realised in dedicated engineering software, whereas this software exchanges information with the 3D visualisation software and a Virtual Reality (VR) headset. Several simulation scenarios have been presented, comparing the proposed method with standard ways for visualising the robot's motion, such as 2D plots and 3D animations on a computer screen. This illustrates how, in the engineering context, this more immersive experience, allowing the viewer to observe the simulation results and modify the simulation parameters within the VR environment, can facilitate the analysis and design of systems.

Robot-Based Calibration Procedure for Graphene Electronic Skin.

The paper describes the semi-automatised calibration procedure of an electronic skin comprising screen-printed graphene-based sensors intended to be used for robotic applications. The variability of sensitivity and load characteristics among sensors makes the practical use of the e-skin extremely difficult. As the number of active elements forming the e-skin increases, this problem becomes more significant. The article describes the calibration procedure of multiple e-skin array sensors whose parameters are not homogeneous. We describe how an industrial robot equipped with a reference force sensor can be used to automatise the e-skin calibration procedure. The proposed methodology facilitates, speeds up, and increases the repeatability of the e-skin calibration. Finally, for the chosen example of a nonhomogeneous sensor matrix, we provide details of the data preprocessing, the sensor modelling process, and a discussion of the obtained results.

Odour concentrations prediction based on odorants concentrations from biosolid emissions.

Biosolids storage areas are a significant contributor to wastewater treatment plant (WWTPs) odour emissions which can cause sensorial impact to surrounding communities. Most odour impact regulations are based on odour concentration (COD) measurements determined by dynamic olfactometry. Understanding the relationship between odorants concentrations and COD in the biosolids emission is important to identify how the measurement and monitoring can be conducted using analytical rather than sensorial techniques. Some of the odorants are unknown in biosolid emissions, increasing the uncertainty in predicting COD. In this study, emissions from 56 biosolid samples collected from two WWTPs located in Sydney, Australia, were analysed by analytical and sensorial methods, including olfactory detection port (ODP) and dynamic olfactometry. Concentrations of 25 odorants and two ordinal variables represented odour events detected by ODP assessors were linked to COD values. Bayesian Model Averaging and Variable Selection with Bayesian Adaptive Sampling were applied to model the relation between COD and odorants concentrations. Results indicate the usability of the probabilistic methods and nonlinear transformations in modelling the odour concentrations based on odorants concentrations from biosolids emission and the accuracy of a small dataset.