Task-Consistent Path Planning for Mobile 3D Printing

In this paper, we explore the problem of task-consistent path planning for printing-in-motion via Mobile Manipulators (MM). MM offer a potentially unlimited planar workspace and flexibility for print operations. However, most existing methods have only mobility to relocate an arm which then prints while stationary. In this paper we present a new fully autonomous path planning approach for mobile material deposition. We use a modified version of Rapidly-exploring Random Tree Star (RRT*) algorithm, which is informed by a constrained Inverse Reachability Map (IRM) to ensure task consistency. Collision avoidance and end-effector reachability are respected in our approach. Our method also detects when a print path cannot be completed in a single execution. In this case it will decompose the path into several segments and reposition the base accordingly

Misclassification Risk and Uncertainty Quantification in Deep Classifiers

In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a classifier’s predictions and reduce the likelihood of acting on erroneous predictions. The second is a novel way to train the classifier such that erroneous classifications are biased towards less risky categories. We combine these two approaches in a principled way. While doing this, we extend evidential deep learning with pignistic probabilities, which are used to quantify uncertainty of classification predictions and model rational decision making under uncertainty.We evaluate the performance of our approach on several image classification tasks. We demonstrate that our approach allows to (i) incorporate misclassification cost while training deep classifiers, (ii) accurately quantify the uncertainty of classification predictions, and (iii) simultaneously learn how to make classification decisions to minimize expected cost of classification errors

Towards model-based control of Parkinson's disease

Modern model-based control theory has led to transformative improvements in our ability to track the nonlinear dynamics of systems that we observe, and to engineer control systems of unprecedented efficacy. In parallel with these developments, our ability to build computational models to embody our expanding knowledge of the biophysics of neurons and their networks is maturing at a rapid rate. In the treatment of human dynamical disease, our employment of deep brain stimulators for the treatment of Parkinson’s disease is gaining increasing acceptance. Thus, the confluence of these three developments—control theory, computational neuroscience and deep brain stimulation—offers a unique opportunity to create novel approaches to the treatment of this disease. This paper explores the relevant state of the art of science, medicine and engineering, and proposes a strategy for model-based control of Parkinson’s disease. We present a set of preliminary calculations employing basal ganglia computational models, structured within an unscented Kalman filter for tracking observations and prescribing control. Based upon these findings, we will offer suggestions for future research and development

Genetic Diversity Among Alfalfa Cultivars Using SSR Markers

Alfalfa (Medicago sativa) is an autotetraploid, allogamous and heterozygous species. Cultivated varieties are synthetic cultivars, usually obtained through 3 or 4 generations of panmictic reproduction of a set of various numbers of parents. The parents can be clones, half-sib or full-sib families. The breeders apply selection pressure for some agronomic traits, to induce changes in the genetic background. The objective of this study was to investigate the differentiation level among seven cultivars originating from one breeding program, and between these cultivars and the breeding pool, with eight SSR markers

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue

Crossing disciplines to address urban sustainability

This paper presents findings from the evaluation of Bridging the Gaps: Sustainable Urban Spaces (BTG), a novel interdisciplinary sustainability research funding program at University College London (UCL), funded by the United Kingdom Engineering and Physical Sciences Research Council (EPSRC). All of the EPSRC's Bridging the Gaps programs aim to initiate and support interdisciplinary collaboration within a university. The program at UCL was designed to create research partnerships that focus on problems in the area of sustainable urban spaces, an area that features complex problems that often overlap different academic disciplines. The program initially focused on building relationships within the three UCL faculties: The Bartlett Faculty of the Built Environment, The Faculty of Engineering Sciences, and The Faculty of Mathematical and Physical Sciences, but subsequently brought in participants from other faculties. Bridging the Gaps has brought together researchers working on different elements of a problem, allowing each of them to contribute approaches from their own discipline. This paper presents feedback from participants in the program. Respondents discuss their experience in cross disciplinary working and its importance for their work. We address the question of whether the benefits are outweighed by the complexities of crossing disciplines, and we investigate the role that programs like BTG can play in making the process easier. We also discuss the challenge of creating the conditions for interdisciplinary work and ways in which we can use our experience to minimize the barriers of crossing disciplines in the future

The modern pollen-vegetation relationship of a tropical forest-savannah mosaic landscape, Ghana, West Africa

Transitions between forest and savannah vegetation types in fossil pollen records are often poorly understood due to over-production by taxa such as Poaceae and a lack of modern pollen-vegetation studies. Here, modern pollen assemblages from within a forest-savannah transition in West Africa are presented and compared, their characteristic taxa discussed, and implications for the fossil record considered. Fifteen artificial pollen traps were deployed for 1 year, to collect pollen rain from three vegetation plots within the forest-savannah transition in Ghana. High percentages of Poaceae and Melastomataceae/Combretaceae were recorded in all three plots. Erythrophleum suaveolens characterised the forest plot, Manilkara obovata the transition plot and Terminalia the savannah plot. The results indicate that Poaceae pollen influx rates provide the best representation of the forest-savannah gradient, and that a Poaceae abundance of >40% should be considered as indicative of savannah-type vegetation in the fossil record

A bank of unscented Kalman filters for multimodal human perception with mobile service robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics

Colour in urban places: A case study of Leicester City Football Club blue

By communicating an integrated story, the Leicester City Football Club blue inherits and persists the history and legacy of the football club, which further provides a stable and consistent meaning for the local sports culture. Colour as a medium and agency creates an intimacy and loyalty between the different ethnic and social groups across local, regional, and global contexts. The case study demonstrated that colour could give place identity through branding practice, identity mediation, and visual culture formation. The process reflected that economic and cultural force had a large impact on place‐making, and could be the decisive influence upon colour symbolism