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.

Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets

Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated sampling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1–28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model diversity for thorough exploration of the model space. This algorithm was applied to a validating example to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission examples of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.

Investigating the link between transit oriented development and sustainable travel behavior in Brisbane : a case-control study

The purpose of this research is to empirically test the prevailing view that transit oriented development enhances the use of more sustainable modes of transport using Brisbane, Australia as a case. Transit oriented development has been adopted as a new policy tool to reduce car-based travel worldwide. Despite being a billion dollar investment, the impacts of transit oriented development on promoting sustainable travel behavior is not conclusive. The research uses a case-control approach to empirically investigate this relationship based on travel behavior data collected from 88 individuals living in two contrasting neighborhoods in Brisbane: Kelvin Grove Urban Village – a transit oriented development, and Annerley – a traditional suburb (non-transit oriented development). A comparative investigation of travel behavior was subsequently conducted using distance travelled by modes and purposes between the neighborhoods. Results show that the availability of opportunity and services located within the transit oriented development reduces the car use by 5% and increases the use of active transport by 4%. The findings in this research support the implementation of TOD policies in Brisbane.

Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

Reduced complexity on-line estimation of hidden Markov model parameters

In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.

Activation of membrane-bound proteins and receptor systems: A link between tissue kallikrein and the KLK-related peptidases

The 15 members of the kallikrein-related serine peptidase (KLK) family have diverse tissue-specific expression profiles and roles in a range of cellular processes, including proliferation, migration, invasion, differentiation, inflammation and angiogenesis that are required in both normal physiology as well as pathological conditions. These roles require cleavage of a range of substrates, including extracellular matrix proteins, growth factors, cytokines as well as other proteinases. In addition, it has been clear since the earliest days of KLK research that cleavage of cell surface substrates is also essential in a range of KLK-mediated cellular processes where these peptidases are essentially acting as agonists and antagonists. In this review we focus on these KLK-regulated cell surface receptor systems including bradykinin receptors, proteinase-activated receptors, as well as the plasminogen activator, ephrins and their receptors, and hepatocyte growth factor/Met receptor systems and other plasma membrane proteins. From this analysis it is clear that in many physiological and pathological settings KLKs have the potential to regulate multiple receptor systems simultaneously; an important issue when these peptidases and substrates are targeted in disease.

ACE research vignette 037 : Bill Gates’ mug shot : Is there a secret link between rule-breaking, crime, and entrepreneurship?

This series of research vignettes is aimed at sharing current and interesting research findings from international entrepreneurship researchers. In this vignette, Dr. Martin Obschonka, considers the relationship between entrepreneurship and rule-breaking.

Investigating the link between carbon dioxide emissions and transport-related social exclusion in rural Northern Ireland

Carbon dioxide (CO2) is considered the most harmful of the greenhouse gases. Despite policy efforts, transport is the only sector experiencing an increase in the level of CO2 emissions and thereby possesses a major threat to sustainable development. In contrast, a reduced level of mobility has been associated with an increasing risk of being socially excluded. However, despite being the two key elements in transport policy, little effort has so far been made to investigate the links between CO2 emissions and social exclusion. This research contributes to this gap by analysing data from 157 weekly activity-travel diaries collected in rural Northern Ireland. CO2 emission levels were calculated using average speed models for different modes of transport. Regression analyses were then conducted to identify the socio-spatial patterns associated with these CO2 emissions, mode choice behaviour, and patterns of participation in activities. This research found that despite emitting a higher level of carbon dioxide, groups in rural areas possess the risk of being socially excluded due to their higher levels of mobility.

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.

Curiosity driven reinforcement learning for motion planning on humanoids

Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment.

Estimating link-dependent origin-destination matrices from sample trajectories and traffic counts

In transport networks, Origin-Destination matrices (ODM) are classically estimated from road traffic counts whereas recent technologies grant also access to sample car trajectories. One example is the deployment in cities of Bluetooth scanners that measure the trajectories of Bluetooth equipped cars. Exploiting such sample trajectory information, the classical ODM estimation problem is here extended into a link-dependent ODM (LODM) one. This much larger size estimation problem is formulated here in a variational form as an inverse problem. We develop a convex optimization resolution algorithm that incorporates network constraints. We study the result of the proposed algorithm on simulated network traffic.

Influence of kaolinite particle size on cross-link density, microstructure and mechanical properties of latex blending styrene butadiene rubber composites

Cross-link density, microstructure and mechanical properties of styrene butadiene rubber (SBR) composites filled with different particle sized kaolinites are investigated. With the increase of kaolinite particle size, the cross-link density of the filled SBR composites, the dispersibility and orientation degree of kaolinite particles gradually decrease. Some big cracks in filled rubber composites are distributed along the fringe of kaolinite aggregates, and the absorbance of all the absorption bands of kaolinites gradually increase with the increase of kaolinite particle size. All mechanical property indexes of kaolinite filled SBR composites decrease due to the decrease of cross-linking and reduction of interface interaction between filler and rubber matrix.

Endurance exercise and DNA stability: Is there a link to duration and intensity?

It is commonly accepted that regular moderate intensity physical activity reduces the risk of developing many diseases. Counter intuitively, however, evidence also exists for oxidative stress resulting from acute and strenuous exercise. Enhanced formation of reactive oxygen and nitrogen species may lead to oxidatively modified lipids, proteins and nucleic acids and possibly disease. Currently, only a few studies have investigated the influence of exercise on DNA stability and damage with conflicting results, small study groups and the use of different sample matrices or methods and result units. This is the first review to address the effect of exercise of various intensities and durations on DNA stability, focusing on human population studies. Furthermore, this article describes the principles and limitations of commonly used methods for the assessment of oxidatively modified DNA and DNA stability. This review is structured according to the type of exercise conducted (field or laboratory based) and the intensity performed (i.e. competitive ultra/endurance exercise or maximal tests until exhaustion). The findings presented here suggest that competitive ultra-endurance exercise (>4h) does not induce persistent DNA damage. However, when considering the effects of endurance exercise (<4h), no clear conclusions could be drawn. Laboratory studies have shown equivocal results (increased or no oxidative stress) after endurance or exhaustive exercise. To clarify which components of exercise participation (i.e. duration, intensity and training status of subjects) have an impact on DNA stability and damage, additional carefully designed studies combining the measurement of DNA damage, gene expression and DNA repair mechanisms before, during and after exercise of differing intensities and durations are required.