Laser-camera data discrepancies and reliable perception in outdoor robotics

This work aims to promote integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicles equipped with a camera and a 2D laser range finder. A method to check for inconsistencies between the data provided by these two heterogeneous sensors is proposed and discussed. First, uncertainties in the estimated transformation between the laser and camera frames are evaluated and propagated up to the projection of the laser points onto the image. Then, for each pair of laser scan-camera image acquired, the information at corners of the laser scan is compared with the content of the image, resulting in a likelihood of correspondence. The result of this process is then used to validate segments of the laser scan that are found to be consistent with the image, while inconsistent segments are rejected. Experimental results illustrate how this technique can improve the reliability of perception in challenging environmental conditions, such as in the presence of airborne dust.

Towards reliable perception for unmanned ground vehicles in challenging conditions

This work aims to promote reliability and integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicle (UGV) autonomy. For this purpose, a comprehensive UGV system, comprising many different exteroceptive and proprioceptive sensors has been built. The first contribution of this work is a large, accurately calibrated and synchronised, multi-modal data-set, gathered in controlled environmental conditions, including the presence of dust, smoke and rain. The data have then been used to analyse the effects of such challenging conditions on perception and to identify common perceptual failures. The second contribution is a presentation of methods for mitigating these failures to promote perceptual integrity in adverse environmental conditions.

A probabilistic framework to monitor a multi-mode outdoor robot

This paper presents an approach to autonomously monitor the behavior of a robot endowed with several navigation and locomotion modes, adapted to the terrain to traverse. The mode selection process is done in two steps: the best suited mode is firstly selected on the basis of initial information or a qualitative map built on-line by the robot. Then, the motions of the robot are monitored by various processes that update mode transition probabilities in a Markov system. The paper focuses on this latter selection process: the overall approach is depicted, and preliminary experimental results are presented

Enhanced locomotion control for a planetary rover

This article presents an approach to improve and monitor the behavior of a skid-steering rover on rough terrains. An adaptive locomotion control generates speeds references to avoid slipping situations. An enhanced odometry provides a better estimation of the distance travelled. A probabilistic classification procedure provides an evaluation of the locomotion efficiency on-line, with a detection of locomotion faults. Results obtained with a Marsokhod rover are presented throughout the paper

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.

Global reconfiguration of a team of networked mobile robots among obstacles

This paper presents a full system demonstration of dynamic sensorbased reconfiguration of a networked robot team. Robots sense obstacles in their environment locally and dynamically adapt their global geometric configuration to conform to an abstract goal shape. We present a novel two-layer planning and control algorithm for team reconfiguration that is decentralised and assumes local (neighbour-to-neighbour) communication only. The approach is designed to be resource-efficient and we show experiments using a team of nine mobile robots with modest computation, communication, and sensing. The robots use acoustic beacons for localisation and can sense obstacles in their local neighbourhood using IR sensors. Our results demonstrate globally-specified reconfiguration from local information in a real robot network, and highlight limitations of standard mesh networks in implementing decentralised algorithms.

Learned stochastic mobility prediction for planning with control uncertainty on unstructured terrain

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an on-board depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and non-traversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and report results from nearly 300 experimental trials using a planetary rover platform in a Mars-analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation.

PSMA-targeting iron oxide magnetic nanoparticles enhance MRI of preclinical prostate cancer

Aim Evaluate potential of newly-developed, biocompatible iron oxide magnetic nanoparticles (MNPs) conjugated with J591, an antibody to an extracellular epitope of prostate specific membrane antigen (PSMA), to enhance MRI of prostate cancer (PCa). Materials & Methods Specific binding to PSMA by J591-MNP was investigated in vitro. MRI studies were performed on orthotopic tumor-bearing NOD.SCID mice 2h and 24hr after intravenous injection of J591-MNPs, or non-targeting MNPs. Results and Conclusions In vitro, MNPs did not affect PCa cell viability, and conjugation to J591 did not compromise antibody specificity and enhanced cellular iron uptake. In vivo, PSMA-targeting MNPs increased MR contrast of tumors, but not by non-targeting MNPs. This provides proof-of-concept that PSMA-targeting MNPs have potential to enhance MR detection/localization of PCa.,

Enhancing MRI of prostate cancer using PSMA-targeting iron oxide magnetic nanoparticles

Introduction Novel imaging techniques for prostate cancer (PCa) are required to improve staging and real-time assessment of therapeutic response. We performed preclinical evaluation of newly-developed, biocompatible magnetic nanoparticles (MNPs) conjugated with J591, an antibody specific for prostate specific membrane antigen (PSMA), to enhance magnetic resonance imaging (MRI) of PCa. PSMA is expressed on ∼90% of PCa, including those that are castrate-resistant, rendering it as a rational target for PCa imaging. Materials and Methods The specificity of J591 for PSMA was confirmed by flow cytometric analysis of several PCa cell lines of known PSMA status. MNPs were prepared, engineered to the appropriate size, labeled with DiR fluorophore, and their toxicity to a panel of PC cells was assessed by in vitro Alamar Blue assay. Immunohistochemistry, fluorescence microscopy and Prussian Blue staining (iron uptake) were used to evaluate PSMA specificity of J591-MNP conjugates. In vivo MRI studies (16.4T MRI system) were performed using live immunodeficient mice bearing orthotopic LNCaP xenografts and injected intravenously with J591-MNPs or MNPs alone. Results MNPs were non-toxic to PCa cells. J591-MNP conjugates showed no compromise in specificity of binding to PSMA+ cells and showed enhanced iron uptake compared with MNPs alone. In vivo, tumour targeting (significant MR image contrast) was evident in mice injected with J591-MNPs, but not MNPs alone. Resected tumours from targeted mice had an accumulation of MNPs, not seen in normal control prostate. Conclusions Application of PSMA-targeting MNPs into conventional MRI has potential to enhance PCa detection and localization in real-time, improving patient management.

Comparative studies made simple in GPFlow

This work describes recent extensions to the GPFlow scientific workflow system in development at MQUTeR (www.mquter.qut.edu.au), which facilitate interactive experimentation, automatic lifting of computations from single-case to collection-oriented computation and automatic correlation and synthesis of collections. A GPFlow workflow presents as an acyclic data flow graph, yet provides powerful iteration and collection formation capabilities.

Analyzing the impact of learning inputs on near-to-far terrain traversability estimation

With the increasing need to adapt to new environments, data-driven approaches have been developed to estimate terrain traversability by learning the rover’s response on the terrain based on experience. Multiple learning inputs are often used to adequately describe the various aspects of terrain traversability. In a complex learning framework, it can be difficult to identify the relevance of each learning input to the resulting estimate. This paper addresses the suitability of each learning input by systematically analyzing the impact of each input on the estimate. Sensitivity Analysis (SA) methods provide a means to measure the contribution of each learning input to the estimate variability. Using a variance-based SA method, we characterize how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We propose an approach built on Analysis of Variance (ANOVA) decomposition to examine the prediction made in a near-to-far learning framework based on multi-task GP regression. We demonstrate the approach by analyzing the impact of driving speed and terrain geometry on the prediction of the rover’s attitude and chassis configuration in a Marsanalogue terrain using our prototype rover Mawson.

Instant coffee with high chlorogenic acid levels protects humans against oxidative damage of macromolecules

Scope: Coffee is among the most frequently consumed beverages. Its consumption is inversely associated to the incidence of diseases related to reactive oxygen species; the phenomenon may be due to its antioxidant properties. Our primary objective was to investigate the impact of consumption of a coffee containing high levels of chlorogenic acids on the oxidation of proteins, DNA and membrane lipids; additionally, other redox biomarkers were monitored in an intervention trial. Methods and results: The treatment group (n=36) consumed instant coffee co-extracted from green and roasted beans, whereas the control consumed water (800 mL/P/day, 5 days). A global statistical analysis of four main biomarkers selected as primary outcomes showed that the overall changes are significant. 8-Isoprostaglandin F2α in urine declined by 15.3%, 3-nitrotyrosine was decreased by 16.1%, DNA migration due to oxidized purines and pyrimidines was (not significantly) reduced in lymphocytes by 12.5 and 14.1%. Other markers such as the total antioxidant capacity were moderately increased; e.g. LDL and malondialdehyde were shifted towards a non-significant reduction. Conclusion: The oxidation of DNA, lipids and proteins associated with the incidence of various diseases and the protection against their oxidative damage may be indicative for beneficial health effects of coffee.