Dynamical downscaling for the southwest of Western Australia using the WRF modelling system

The southwest of Western Australia (SWWA) is a region of significant cereal production, with the main crops being winter grown wheat and barley. The most important factors influencing wheat growth and production are temperature extremes and precipitation, and hence, it is critical to have an understanding of how these environmental factors have changed in the past, and how they are likely to change in the future. One method of addressing this important research question is by using regional climate models (RCMs) to dynamically downscale re-analysis products and/or output form Global Circulation Models to a fine resolution. One tool which is being increasingly used for this purpose is the Weather Research and Forecasting Model (WRF) Advanced Research (ARW). However, like any modeling system, WRF-ARW requires thorough testing before it is implemented to carry out long-term climate runs. This paper examines the influence of different input data sources, as well as model physics options on simulated precipitation and maximum and minimum temperatures in SWWA by comparing the simulations against an observational gridded dataset. It is found that running WRF3.3 with the 1.0 × 1.0 degree National Center for Environmental Prediction Final analysis (NCEP-FNL), as compared to the 2.5 × 2.5 degree NCEP / National Center for Atmospheric Research (NCEP/NCAR or NNRP) results in much improved simulations of precipitation and temperatures. Using the National Oceanic and Atmospheric Administration 1.0 × 1.0 degree resolution sea surface temperature (SST) dataset does not result in markedly different results as compared to using the NNRP surface skin temperatures as SSTs. Using the Betts-Miller-Jajic (BMJ) scheme for cumulus/convection parameterisation rather than the more widely used Kain Fritsch (KF) scheme results in slightly higher errors for precipitation, and no marked change in temperatures. The latest version of the Rapid Radiative Transfer Model (RRTMG) is found to result in improved simulations of maximum and minimum temperatures, as compared to the RRTM, Community Atmosphere Model (CAM) 3.0, and Dudhia schemes. Use of the Asymmetric Convective Model as the planetary boundary-layer scheme rather than the more widely used Yonsei University scheme results in over-prediction of maximum and minimum temperatures

Whisker-object contact speed affects radial distance estimation

Whiskered mammals such as rats are experts in tactile perception. By actively palpating surfaces with their whiskers, rats and mice are capable of acute texture discrimination and shape perception. We present a novel system for investigating whisker-object contacts repeatably and reliably. Using an XY positioning robot and a biomimetic artificial whisker we can generate signals for different whisker-object contacts under a wide range of conditions. Our system is also capable of dynamically altering the velocity and direction of the contact based on sensory signals. This provides a means for investigating sensory motor interaction in the tactile domain. Here we implement active contact control, and investigate the effect that speed has on radial distance estimation when using different features for classification. In the case of a moving object contacting a whisker, magnitude of deflection can be ambiguous in distinguishing a nearby object moving slowly from a more distant object moving rapidly. This ambiguity can be resolved by finding robust features for contact speed, which then informs classification of radial distance. Our results are verified on a dataset from SCRATCHbot, a whiskered mobile robot. Building whiskered robots and modelling these tactile perception capabilities would allow exploration and navigation in environments where other sensory modalities are impaired, for example in dark, dusty or loud environments such as disaster areas. © 2010 IEEE

Whiskered texture classification with uncertain contact pose geometry

Tactile sensing can be an important source of information for robots, and texture discrimination in particular is useful in object recognition and terrain identification. Whisker based tactile sensing has recently been shown to be a promising approach for mobile robots, using simple sensors and many classification approaches. However these approaches have often been tested in limited environments, and have not been compared against one another in a controlled way. A wide range of whisker-object contact poses are possible on a mobile robot, and the effect such contact variability has on sensing has not been properly investigated. We present a novel, carefully controlled study of simple surface texture classifiers on a large set of varied pose conditions that mimic those encountered by mobile robots. Namely, single brief whisker contacts with textured surfaces at a range of surface orientations and contact speeds. Results show that different classifiers are appropriate for different settings, with spectral template and feature based approaches performing best in surface texture, and contact speed estimation, respectively. The results may be used to inform selection of classifiers in tasks such as tactile SLAM

Milagro Version 2 An Implicit Monte Carlo Code for Thermal Radiative Transfer: Capabilities, Development, and Usage

We have released Version 2 of Milagro, an object-oriented, C++ code that performs radiative transfer using Fleck and Cummings' Implicit Monte Carlo method. Milagro, a part of the Jayenne program, is a stand-alone driver code used as a methods research vehicle and to verify its underlying classes. These underlying classes are used to construct Implicit Monte Carlo packages for external customers. Milagro-2 represents a design overhaul that allows better parallelism and extensibility. New features in Milagro-2 include verified momentum deposition, restart capability, graphics capability, exact energy conservation, and improved load balancing and parallel efficiency. A users' guide also describes how to configure, make, and run Milagro2

Naive Bayes texture classification applied to whisker data from a moving robot

Many rodents use their whiskers to distinguish objects by surface texture. To examine possible mechanisms for this discrimination, data from an artificial whisker attached to a moving robot was used to test texture classification algorithms. This data was examined previously using a template-based classifier of the whisker vibration power spectrum [1]. Motivated by a proposal about the neural computations underlying sensory decision making [2], we classified the raw whisker signal using the related ‘naive Bayes’ method. The integration time window is important, with roughly 100ms of data required for good decisions and 500ms for the best decisions. For stereotyped motion, the classifier achieved hit rates of about 80% using a single (horizontal or vertical) stream of vibration data and 90% using both streams. Similar hit rates were achieved on natural data, apart from a single case in which the performance was only about 55%. Therefore this application of naive Bayes represents a biologically motivated algorithm that can perform well in a real-world robot task

CrunchBot : a mobile whiskered robot platform

CrunchBot is a robot platform for developing models of tactile perception and navigation. We present the architecture of CrunchBot, and show why tactile navigation is difficult. We give novel real-time performance results from components of a tactile navigation system and a description of how they may be integrated at a systems level. Components include floor surface classification, radial distance estimation and navigation. We show how tactile-only navigation differs fundamentally from navigation tasks using vision or laser sensors, in that the assumptions about the data preclude standard algorithms (such as extended Kalman Filters) and require brute-force methods

Volume 32, Number 4, December 2012 OLAC Newsletter

Digitized December 2012 issue of the OLAC Newsletter

Embodied hyperacuity from Bayesian perception: Shape and position discrimination with an iCub fingertip sensor

Recent advances in modeling animal perception has motivated an approach of Bayesian perception applied to biomimetic robots. This study presents an initial application of Bayesian perception on an iCub fingertip sensor mounted on a dedicated positioning robot. We systematically probed the test system with five cylindrical stimuli offset by a range of positions relative to the fingertip. Testing the real-time speed and accuracy of shape and position discrimination, we achieved sub-millimeter accuracy with just a few taps. This result is apparently the first explicit demonstration of perceptual hyperacuity in robot touch, in that object positions are perceived more accurately than the taxel spacing. We also found substantial performance gains when the fingertip can reposition itself to avoid poor perceptual locations, which indicates that improved robot perception could mimic active perception in animals