Golf ball picker robot: path generation in unstructured environments towards multiple targets

Tese de doutoramento Engineering Design and Advanced Manufacturing Leaders for Technical IndustriesThe new TWIN-RRT* algorithm solves a motion planning problem in which an agent has multiple possible targets where none of them is compulsory, and retrieves feasible, ―low cost‖, asymptotically optimal and probabilistically complete paths. The TWIN-RRT* algorithm solves path planning problems for both holonomic and non-holonomic robots with or without kinodynamic constraints in a 2D environment, but it was designed to work as well with higher DOF agents and different applications. The new algorithm provides a practical implementation of feasible and fast planning especially where a closed loop is required. Initial and final configurations are allowed to be exactly the same. The TWIN-RRT* algorithm computes an efficient path for one sole agent towards multiple targets where none of them is mandatory. It inherits the low computational cost, probabilistic completeness and asymptotical optimality from RRT*. It uses efficiency as cost function, which can be adapted depending on the application. The TWIN-RRT* complies both with kinodynamic constraints and different cost functions. It was developed to solve a real problem where a robot has to collect golf balls in a driving range, where thousands of balls accumulate every day. This thesis is part of a bigger project, Golfminho, to develop an autonomous robot capable of efficiently collecting balls in a golf practice field.O novo algoritmo TWIN-RRT* resolve problemas de planeamento de trajetórias em que um agente tem múltiplos alvos, onde nenhum deles é obrigatório, e produz um plano exequível, de "baixo custo" computacional, assintoticamente ótimo e probabilisticamente completo. O TWINRRT* resolve problemas de planeamento de trajetórias tanto para robôs holonómicos como não holonómicos com ou sem restrições cinemáticas e/ou dinâmicas num ambiente 2D, mas foi projetado para funcionar também com agentes com maiores graus de liberdade e em diferentes aplicações. O novo algoritmo fornece uma implementação prática de um planeamento viável e rápido, especialmente quando é necessário produzir uma trajetória fechada. As configurações iniciais e finais podem ser exatamente iguais. O algoritmo TWIN-RRT* calcula um caminho eficiente para um agente único em direção a múltiplos alvos, onde nenhum deles é obrigatório. Herda o baixo custo computacional, integralidade probabilística e otimização assintótica do RRT*. Usa a eficiência como função de custo, que pode ser adaptada em função das diferentes aplicações. Para além de diferentes funções de custo, o TWIN-RRT* também mostra conformidade com restrições cinemáticas. Foi desenvolvido para resolver um problema real em que um robô tem que recolher bolas de golfe num Driving Range, onde se acumulam milhares de bolas de golfe por dia. Esta tese é parte integrante do projeto Golfminho, para o desenvolvimento de um robô autónomo capaz de recolher eficientemente bolas num campo de práticas de golfe.Fundação para a Ciência e Tecnologia (FCT) for the PhD grant nº. SFRH/BD/43008/2008

Legged Robots for Object Manipulation: A Review

Legged robots can have a unique role in manipulating objects in dynamic, human-centric, or otherwise inaccessible environments. Although most legged robotics research to date typically focuses on traversing these challenging environments, many legged platform demonstrations have also included "moving an object" as a way of doing tangible work. Legged robots can be designed to manipulate a particular type of object (e.g., a cardboard box, a soccer ball, or a larger piece of furniture), by themselves or collaboratively. The objective of this review is to collect and learn from these examples, to both organize the work done so far in the community and highlight interesting open avenues for future work. This review categorizes existing works into four main manipulation methods: object interactions without grasping, manipulation with walking legs, dedicated non-locomotive arms, and legged teams. Each method has different design and autonomy features, which are illustrated by available examples in the literature. Based on a few simplifying assumptions, we further provide quantitative comparisons for the range of possible relative sizes of the manipulated object with respect to the robot. Taken together, these examples suggest new directions for research in legged robot manipulation, such as multifunctional limbs, terrain modeling, or learning-based control, to support a number of new deployments in challenging indoor/outdoor scenarios in warehouses/construction sites, preserved natural areas, and especially for home robotics.Comment: Preprint of the paper submitted to Frontiers in Mechanical Engineerin

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Modelado de sensores piezoresistivos y uso de una interfaz basada en guantes de datos para el control de impedancia de manipuladores robóticos

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Arquitectura de Computadores y Automática, leída el 21-02-2014Sección Deptal. de Arquitectura de Computadores y Automática (Físicas)Fac. de Ciencias FísicasTRUEunpu

Learning domain abstractions for long lived robots

Recent trends in robotics have seen more general purpose robots being deployed in unstructured environments for prolonged periods of time. Such robots are expected to adapt to different environmental conditions, and ultimately take on a broader range of responsibilities, the specifications of which may change online after the robot has been deployed. We propose that in order for a robot to be generally capable in an online sense when it encounters a range of unknown tasks, it must have the ability to continually learn from a lifetime of experience. Key to this is the ability to generalise from experiences and form representations which facilitate faster learning of new tasks, as well as the transfer of knowledge between different situations. However, experience cannot be managed na¨ıvely: one does not want constantly expanding tables of data, but instead continually refined abstractions of the data – much like humans seem to abstract and organise knowledge. If this agent is active in the same, or similar, classes of environments for a prolonged period of time, it is provided with the opportunity to build abstract representations in order to simplify the learning of future tasks. The domain is a common structure underlying large families of tasks, and exploiting this affords the agent the potential to not only minimise relearning from scratch, but over time to build better models of the environment. We propose to learn such regularities from the environment, and extract the commonalities between tasks. This thesis aims to address the major question: what are the domain invariances which should be learnt by a long lived agent which encounters a range of different tasks? This question can be decomposed into three dimensions for learning invariances, based on perception, action and interaction. We present novel algorithms for dealing with each of these three factors. Firstly, how does the agent learn to represent the structure of the world? We focus here on learning inter-object relationships from depth information as a concise representation of the structure of the domain. To this end we introduce contact point networks as a topological abstraction of a scene, and present an algorithm based on support vector machine decision boundaries for extracting these from three dimensional point clouds obtained from the agent’s experience of a domain. By reducing the specific geometry of an environment into general skeletons based on contact between different objects, we can autonomously learn predicates describing spatial relationships. Secondly, how does the agent learn to acquire general domain knowledge? While the agent attempts new tasks, it requires a mechanism to control exploration, particularly when it has many courses of action available to it. To this end we draw on the fact that many local behaviours are common to different tasks. Identifying these amounts to learning “common sense” behavioural invariances across multiple tasks. This principle leads to our concept of action priors, which are defined as Dirichlet distributions over the action set of the agent. These are learnt from previous behaviours, and expressed as the prior probability of selecting each action in a state, and are used to guide the learning of novel tasks as an exploration policy within a reinforcement learning framework. Finally, how can the agent react online with sparse information? There are times when an agent is required to respond fast to some interactive setting, when it may have encountered similar tasks previously. To address this problem, we introduce the notion of types, being a latent class variable describing related problem instances. The agent is required to learn, identify and respond to these different types in online interactive scenarios. We then introduce Bayesian policy reuse as an algorithm that involves maintaining beliefs over the current task instance, updating these from sparse signals, and selecting and instantiating an optimal response from a behaviour library. This thesis therefore makes the following contributions. We provide the first algorithm for autonomously learning spatial relationships between objects from point cloud data. We then provide an algorithm for extracting action priors from a set of policies, and show that considerable gains in speed can be achieved in learning subsequent tasks over learning from scratch, particularly in reducing the initial losses associated with unguided exploration. Additionally, we demonstrate how these action priors allow for safe exploration, feature selection, and a method for analysing and advising other agents’ movement through a domain. Finally, we introduce Bayesian policy reuse which allows an agent to quickly draw on a library of policies and instantiate the correct one, enabling rapid online responses to adversarial conditions

Interfaces for human-centered production and use of computer graphics assets

L'abstract è presente nell'allegato / the abstract is in the attachmen

Face and object recognition by 3D cortical representations

This thesis presents a novel integrated cortical architecture with significant emphasis on low-level attentional mechanisms—based on retinal nonstandard cells and pathways—that can group non-attentional, bottom-up features present in V1/V2 into “proto-object” shapes. These shapes are extracted at first using combinations of specific cell types for detecting corners, bars/edges and curves which work extremely well for geometrically shaped objects. Later, in the parietal pathway (probably in LIP), arbitrary shapes can be extracted from population codes of V2 (or even dorsal V3) oriented cells that encode the outlines of objects as “proto-objects”. Object shapes obtained at both cortical levels play an important role in bottom-up local object gist vision, which tries to understand scene context in less than 70 ms and is thought to use both global and local scene features. Edge conspicuity maps are able to detect borders/edges of objects and attribute them a weight based on their perceptual salience, using readily available retinal ganglion cell colour-opponency coding. Conspicuity maps are fundamental in building posterior saliency maps—important for both bottom-up attention schemes and also for Focus-of-Attention mechanisms that control eye gaze and object recognition. Disparity maps are also a main focus of this thesis. They are built upon binocular simple and complex cells in quadrature, using a Disparity-Enery Model. These maps are fundamental for perception of distance within a scene and close/far object relationships in doing foreground to background segregation. The role of cortical disparity in 3D facial recognition was also explored when processing faces with very different facial expressions (even extreme ones), yielding state-of-the-art results when compared to other, non-biological, computer vision algorithms.A presente tese descreve uma nova arquitectura cortical integrada, com ênfase especial em mecanismos de atenção a baixo nível—baseados em conexões corticais que utilizam células retinais não-padronizadas—conseguindo agrupar diversas características visuais de baixo nível, ainda num estado pré-atencional, presentes nas áreas V1/V2, em formas específicas de “proto-objectos”. As formas em questão são extraídas em primeira mão através de combinações de células especializadas que detectam localmente cantos, rectas/arestas e curvaturas, funcionando extremamente bem para a detecção de objectos com formas geométricas. Posteriormente, no lobo parietal (provavelmente no córtex Lateral Intra-Parietal), já podem ser extraídas formas arbitrárias, através de padrões de activação de populações de neurónios, presentes em V2 (ou até em V3-dorsal), que codificam a periferia de objectos como “proto-objectos”—representações básicas de categorias específicas de objectos no cérebro. Ambas as formas extraídas nos dois tipos de processamento cortical (utilizando células específicas ou uma codificação de formas arbitrária) desempenham um papel importante na visão gist local, que tenta compreender o contexto geral da cena apresentada ao sistema visual, em menos de 70 ms, sendo esperado que para tal se usem tanto características visuais globais como locais. São também utilizados mapas de conspicuicidade, que permitem detectar linhas e arestas de objectos, atribuindo-lhes um peso baseado na sua saliência perceptual—utilizando para tal a codificação natural das células retinais, em que as cores são representadas por oponência: claro/escuro, vermelho/verde e amarelo/azul. Os mapas de conspicuicidade são fundamentais na construção posterior de mapas de saliência—importantes nos esquemas pré-atencionais de nível celular baixo e também para os mecanisix mos de Foco-de-Atenção que controlam o movimento ocular e reconhecimento de caras e objectos. Em paralelo, são também desenvolvidos os mapas de disparidade cortical, sendo estes também um dos maiores focos desta tese. Estes são baseados em células corticais binoculares simples e complexas, através de um processamento das últimas em quadratura—modelo denominado por “Disparity- Energy Model”. Estes mapas de disparidade são fundamentais na percepção de distâncias dentro de uma cena visual e também para resolver o problema da segregação objecto/fundo. O papel da disparidade cortical é também explorado no reconhecimento facial a 3D, em especial quando as faces a reconhecer apresentam expressões faciais de diversas formas e níveis de intensidade. O modelo utilizado apresentou resultados excelentes, atingindo o estado-da-arte, inclusivamente ficando acima de modelos de visão computacional não biológicos.Fundação para a Ciência e a TecnologiaComissão Europei

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions