Traversability estimation for a planetary rover via experimental kernel learning in a Gaussian process framework

A critical requirement for safe autonomous navigation of a planetary rover is the ability to accurately estimate the traversability of the terrain. This work considers the problem of predicting the attitude and configuration angles of the platform from terrain representations that are often incomplete due to occlusions and sensor limitations. Using Gaussian Processes (GP) and exteroceptive data as training input, we can provide a continuous and complete representation of terrain traversability, with uncertainty in the output estimates. In this paper, we propose a novel method that focuses on exploiting the explicit correlation in vehicle attitude and configuration during operation by learning a kernel function from vehicle experience to perform GP regression. We provide an extensive experimental validation of the proposed method on a planetary rover. We show significant improvement in the accuracy of our estimation compared with results obtained using standard kernels (Squared Exponential and Neural Network), and compared to traversability estimation made over terrain models built using state-of-the-art GP techniques.

Rapid differentiation of isomeric lipids by photodissociation mass spectrometry of fatty acid derivatives

RATIONALE Both traditional electron ionization and electrospray ionization tandem mass spectrometry have demonstrated limitations in the unambiguous identification of fatty acids. In the former case, high electron energies lead to extensive dissociation of the radical cations from which little specific structural information can be obtained. In the latter, conventional collision-induced dissociation (CID) of even-electron ions provides little intra-chain fragmentation and thus few structural diagnostics. New approaches that harness the desirable features of both methods, namely radical-driven dissociation with discrete energy deposition, are thus required. METHODS Herein we describe the derivatization of a structurally diverse suite of fatty acids as 4-iodobenzyl esters (FAIBE). Electrospray ionization of these derivatives in the presence of sodium acetate yields abundant [M+Na]+ ions that can be mass-selected and subjected to laser irradiation (=266nm) on a modified linear ion-trap mass spectrometer. RESULTS Photodissociation (PD) of the FAIBE derivatives yields abundant radical cations by loss of atomic iodine and in several cases selective dissociation of activated carboncarbon bonds (e.g., at allylic positions) are also observed. Subsequent CID of the [M+NaI]center dot+ radical cations yields radical-directed dissociation (RDD) mass spectra that reveal extensive carboncarbon bond dissociation without scrambling of molecular information. CONCLUSIONS Both PD and RDD spectra obtained from derivatized fatty acids provide a wealth of structural information including the position(s) of unsaturation, chain-branching and hydroxylation. The structural information obtained by this approach, in particular the ability to rapidly differentiate isomeric lipids, represents a useful addition to the lipidomics tool box. Copyright (c) 2013 John Wiley & Sons, Ltd.

Bisresorcinols and arbutin derivatives from Grevillea banksii R. Br

This work is part of a series of chemical investigations of the genus Grevillea. Two new arbutin derivatives, seven new bisresorcinols, including a mixture of two isomers, three known flavonol glycosides, and four known resorcinols, including a mixture of two homologous compounds, were isolated from the ethyl acetate extract of the leaves and methanol extract of the stems of Grevillea banksii. The new compounds were identified, on the basis of spectroscopic data, as 6'-O-(3-(2(hydroxymethyl)acryloyloxy)-2-methylpropanoyl)arbutin (1), 6'-O-(2-methylacryloyl)arbutin (2), 5,5'-(4(Z)-dodecen-1,12diyl)bisresorcinol (6), 2'-methyl-5,5'-(4(Z)-tetradecen-1,14-diyl)bisresorcinol (8), 2,2'-di(4-hydroxyprenyl)-5,5'-(6(Z)-tetradecen-1,14-diyl)bisresorcinol (9), 2-(4-acetoxyprenyl)-2'-(4-hydroxyprenyl) 5,5'-(6(Z)-tetradecen-1,14-diyl)bisresorcinol (10), 2-(4-acetoxyprenyl)-2'-(4-hydroxyprenyl)5,5'-(8(Z)-tetradecen-l,14-diyl)bisresorcinol (11), 5,5'-(10(Z)-tetradecen-1-on-diyl)bisresorcinol (12) and 5,5'-(4(Z)-tetradecen-1-on-diyl)bisresorcinol (13).

Bisresorcinol Derivatives from Grevillea glauca

Seven new and three known bisresorcinols, grevirobstol A(=5,5'-((6Z,9Z)-hexadeca-6,9-diene-1,16-diyl)bisresorcinol; 8), 5,5'-[(8Z)-hexadec-8-ene-1,16-diyl]bisresorcinol (9), and 2-methyl-5,5'-[8Z)-hexadec-8-ene-1,16-diyl] bisresorcinol (10) were isolated from the stems of Grevillea glauca. The new compounds were identified on the basis of spectroscopic data as (Z)-6,7-didehydroglaucone A (1), glaucones A and B (2 and 3, resp.), 2-(3-hydroxyisopentyl)bisnorstriatol (4), 2-(3-methylbut-2-en-1-yl)bisnorstriatol (5), 2'-methylgrebustol A (6), and glaucane (7).

Antibacterial anthranilic acid derivatives from Geijera parviflora

Five anthranilic acid derivatives, a mixture I of three new compounds 11′-hexadecenoylanthranilic acid (1), 9′-hexadecenoylanthranilic acid (2), and 7′-hexadecenoylanthranilic acid (3), as well as a new compound 9,12,15-octadecatrienoylanthranilic acid (4) together with a new natural product, hexadecanoylanthranilic acid (5), were isolated from Geijera parviflora Lindl. (Rutaceae). Their structures were elucidated by extensive spectroscopic measurements, and the positions of the double bonds in compounds 1-3 of the mixture I were determined by tandem mass spectrometry employing ozone-induced dissociation. The mixture I and compound 5 showed good antibacterial activity against several Gram-positive strains. © 2013 Elsevier B.V.

Fast re-parameterisation of Gaussian mixture models for robotics applications

Autonomous navigation and picture compilation tasks require robust feature descriptions or models. Given the non Gaussian nature of sensor observations, it will be shown that Gaussian mixture models provide a general probabilistic representation allowing analytical solutions to the update and prediction operations in the general Bayesian filtering problem. Each operation in the Bayesian filter for Gaussian mixture models multiplicatively increases the number of parameters in the representation leading to the need for a re-parameterisation step. A computationally efficient re-parameterisation step will be demonstrated resulting in a compact and accurate estimate of the true distribution.

Oxidation of 4-substituted TEMPO derivatives reveals modifications at the 1-and 4-positions

Potenital pathways for the deactivation of hindered amine light stabilisers (HALS) have been investigated by observing reactions of model compounds-based on 4-substituted derivatives of 2,2,6,6-tetramethylpiperidine-N-oxyl (TEMPO)-with hydroxyl radicals. In these reactions, dilute aqueous suspensions of photocatalytic nanoparticulate titanium dioxide were irradiated with UV light in the presence of water-soluble TEMPO derivatives. Electron spin resonance (ESR) and electrospray ionisation mass-spectrometry (ESI-MS) data were acquired to provide complementary structural elucidation of the odd-and even-electron products of these reactions and both techniques show evidence for the formation of 4-oxo-TEMPO (TEMPONE). TEMPONE formation from the 4-substituted TEMPO compounds is proposed to be initiated by hydrogen abstraction at the 4-position by hydroxyl radical. High-level ab initio calculations reveal a thermodynamic preference for abstraction of this hydrogen but computed activation barriers indicate that, although viable, it is less favoured than hydrogen abstraction from elsewhere on the TEMPO scaffold. If a radical is formed at the 4-position however, calculations elucidate two reaction pathways leading to TEMPONE following combination with either a second hydroxyl radical or dioxygen. An alternate mechanism for conversion of TEMPOL to TEMPONE via an alkoxyl radical intermediate is also considered and found to be competitive with the other pathways. ESI-MS analysis also shows an increased abundance of analogous 4-substituted piperidines during the course of irradiation, suggesting competitive modification at the 1-position to produce a secondary amine. This modification is confirmed by characteristic fragmentation patterns of the ionised piperidines obtained by tandem mass spectrometry. The conclusions describe how reaction at the 4-position could be responsible for the gradual depletion of HALS in pigmented surface coatings and secondly, that modification at nitrogen to form the corresponding secondary amine species may play a greater role in the stabilisation mechanisms of HALS than previously considered.

Production of dihydrosterculic acid and derivatives thereof

The present invention relates to recombinant cells, particularly recombinant plant cells, which are capable of producing dihydrosterculic acid and/or derivatives thereof. The present invention also relates to methods of producing oil comprising dihydrosterculic acid and/or derivatives thereof.

Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces

The ability to build high-fidelity 3D representations of the environment from sensor data is critical for autonomous robots. Multi-sensor data fusion allows for more complete and accurate representations. Furthermore, using distinct sensing modalities (i.e. sensors using a different physical process and/or operating at different electromagnetic frequencies) usually leads to more reliable perception, especially in challenging environments, as modalities may complement each other. However, they may react differently to certain materials or environmental conditions, leading to catastrophic fusion. In this paper, we propose a new method to reliably fuse data from multiple sensing modalities, including in situations where they detect different targets. We first compute distinct continuous surface representations for each sensing modality, with uncertainty, using Gaussian Process Implicit Surfaces (GPIS). Second, we perform a local consistency test between these representations, to separate consistent data (i.e. data corresponding to the detection of the same target by the sensors) from inconsistent data. The consistent data can then be fused together, using another GPIS process, and the rest of the data can be combined as appropriate. The approach is first validated using synthetic data. We then demonstrate its benefit using a mobile robot, equipped with a laser scanner and a radar, which operates in an outdoor environment in the presence of large clouds of airborne dust and smoke.

Enhanced stochastic mobility prediction with multi-output Gaussian processes

Outdoor robots such as planetary rovers must be able to navigate safely and reliably in order to successfully perform missions in remote or hostile environments. Mobility prediction is critical to achieving this goal due to the inherent control uncertainty faced by robots traversing natural terrain. We propose a novel algorithm for stochastic mobility prediction based on multi-output Gaussian process regression. Our algorithm considers the correlation between heading and distance uncertainty and provides a predictive model that can easily be exploited by motion planning algorithms. We evaluate our method experimentally and report results from over 30 trials in a Mars-analogue environment that demonstrate the effectiveness of our method and illustrate the importance of mobility prediction in navigating challenging terrain.

Solution processable low bandgap diketopyrrolopyrrole (DPP) based derivatives : novel acceptors for organic solar cells

Novel low bandgap solution processable diketopyrrolopyrrole (DPP) based derivatives functionalized with electron withdrawing end capping groups (trifluoromethylphenyl and trifluorophenyl) were synthesized, and their photophysical, electrochemical and photovoltaic properties were investigated. These compounds showed optical bandgaps ranging from 1.81 to 1.94 eV and intense absorption bands that cover a wide range from 300 to 700 nm, attributed to charge transfer transition between electron rich phenylene-thienylene moieties and the electron withdrawing diketopyrrolopyrrole core. All of the compounds were found to be fluorescent in solution with an emission wavelength ranging from 600 to 800 nm. Cyclic voltammetry indicated reversible oxidation and reduction processes with tuning of HOMO-LUMO energy levels. Bulk heterojunction (BHJ) solar cells using poly(3-hexylthiophene) (P3HT) as the electron donor with these new acceptors were used for fabrication. The best power conversion efficiencies (PCE) using 1:2 donor-acceptor by weight mixture were 1% under simulated AM 1.5 solar irradiation of 100 mW cm-2. These findings suggested that a DPP core functionalized with electron accepting end-capping groups were a promising new class of solution processable low bandgap n-type organic semiconductors for organic solar cell applications.

Fabrication of optochemical and electrochemical sensors using thin films of porphyrin and phthalocyanine derivatives

This paper describes the fabrication of thin films of porphyrin and metallophthalocyanine derivatives on different substrates for the optochemical detection of HCl gas and electrochemical determination of L-cysteine (CySH). Solid state gas sensor for HCl gas was fabricated by coating meso-substituted porphyrin derivatives on glass slide and examined optochemical sensing of HCl gas. The concentration of gaseous HCl was monitored from the changes in the absorbance of Soret band. Among the different porphyrin derivatives, meso- tetramesitylporphyrin (MTMP) coated film showed excellent sensitivity towards HCl and achieved a detection limit of 0.03ppm HCl. Further, we have studied the self-assembly of 1,8,15,22-tetraaminometallophthalocyanine (4α-MTAPc; M = Co and Ni) from DMF on GC electrode. The CVs for the self-assembled monolayers (SAMs) of 4α-CoIITAPc and 4α-NiIITAPc show two pairs of well-defined redox couple corresponding to metal and ring. Using the 4α-CoIITAPc SAM modified electrode, sensitive and selective detection of L-cysteine was demonstrated. Further, the SAM modified electrode also successfully separates the oxidation potentials of AA and CySH with a peak separation of 320mV.

A Gaussian pseudolikelihood approach for quantile regression with repeated measurements

To enhance the efficiency of regression parameter estimation by modeling the correlation structure of correlated binary error terms in quantile regression with repeated measurements, we propose a Gaussian pseudolikelihood approach for estimating correlation parameters and selecting the most appropriate working correlation matrix simultaneously. The induced smoothing method is applied to estimate the covariance of the regression parameter estimates, which can bypass density estimation of the errors. Extensive numerical studies indicate that the proposed method performs well in selecting an accurate correlation structure and improving regression parameter estimation efficiency. The proposed method is further illustrated by analyzing a dental dataset.

Hydraulic conductivity fields: Gaussian or not?

Hydraulic conductivity (K) fields are used to parameterize groundwater flow and transport models. Numerical simulations require a detailed representation of the K field, synthesized to interpolate between available data. Several recent studies introduced high-resolution K data (HRK) at the Macro Dispersion Experiment (MADE) site, and used ground-penetrating radar (GPR) to delineate the main structural features of the aquifer. This paper describes a statistical analysis of these data, and the implications for K field modeling in alluvial aquifers. Two striking observations have emerged from this analysis. The first is that a simple fractional difference filter can have a profound effect on data histograms, organizing non-Gaussian ln K data into a coherent distribution. The second is that using GPR facies allows us to reproduce the significantly non-Gaussian shape seen in real HRK data profiles, using a simulated Gaussian ln K field in each facies. This illuminates a current controversy in the literature, between those who favor Gaussian ln K models, and those who observe non-Gaussian ln K fields. Both camps are correct, but at different scales.

Autonomous vehicle path planning for persistence monitoring under uncertainty using Gaussian based Markov decision process

One of the main challenges facing online and offline path planners is the uncertainty in the magnitude and direction of the environmental energy because it is dynamic, changeable with time, and hard to forecast. This thesis develops an artificial intelligence for a mobile robot to learn from historical or forecasted data of environmental energy available in the area of interest which will help for a persistence monitoring under uncertainty using the developed algorithm.