GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model

Central to robot exploration and mapping is the task of persistent localization in environmental fields characterized by spatially correlated measurements. This paper presents a Gaussian process localization (GP-Localize) algorithm that, in contrast to existing works, can exploit the spatially correlated field measurements taken during a robot's exploration (instead of relying on prior training data) for efficiently and scalably learning the GP observation model online through our proposed novel online sparse GP. As a result, GP-Localize is capable of achieving constant time and memory (i.e., independent of the size of the data) per filtering step, which demonstrates the practical feasibility of using GPs for persistent robot localization and autonomy. Empirical evaluation via simulated experiments with real-world datasets and a real robot experiment shows that GP-Localize outperforms existing GP localization algorithms.Comment: 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Extended version with proofs, 10 page

STUDI TENTANG PEMODELAN ARUS LALU LINTAS

Permasalahan transportasi saat ini masih menjadi pemasalahan utama pada setiap negara, khususnya negara berkembang. Masalah transportasi dihadapkan pada fenomena kemacetan, banyaknya polusi yang dihasilkan oleh kendaraan, sampai kepada masih tingginya tingkat kecelakaan lalu lintas tiap tahunnya. Hal ini, bukan saja disebabkan oleh perilaku pengemudi jalan raya saja, akan tetapi perencanaan arus lalu lintas pun menjadi salah satu faktor yang mempengaruhinya. Salah satu alternatif penyelesaian untuk dapat mengatur dan memanajemen arus lalu lintas adalah dengan memodelkan arus lalu lintas serta mensimulasikannya dalam komputer sehingga dapat diperoleh prediksi-prediksi yang akan terjadi pada simulasi tersebut. Studi literatur mengenai pemodelan dan simulasi arus lalu lintas terus berkembang sejak setengah abad yang lalu dalam upaya memperoleh sebuah pemodelan yang akurat dan mewakili fenomena yang terjadi sebenarnya. Pemodelan arus lalu lintas berbasis komputer dapat dibagi menjadi tiga skala utama, yaitu: mikroskopik, mesoskopik dan makroskopik. Pada skala mikroskopik, pemodelan arus lalu lintas digambarkan sedetail mungkin yang mencakup perilaku setiap kendaraan dan interaksinya. Pada paper ini dilakukan survey terhadap penelitian terdahulu yang membahas mengenai pemodelan arus lalu lintas pada skala mikroskopik. Pada bagian pertama akan dijelaskan gambaran dan pemahaman mengenai pemodelan arus lalu lintas, pemahaman mengenai model mikroskopik arus dan beberapa penelitian mengenai model yang sudah dikembangkan untuk simulasi mikroskopik beberapa tahun terakhir. Selanjutnya, dilakukan pembahasan mengenai pemodelan arus mikroskopik dihubungkan dengan permasalahan transportasi yang ada di Indonesia. Pada paper ini juga memberikan kemungkinan pengembangan penelitian lebih lanjut untuk model mikroskopik lalu lintas

Spatial crowdsourcing with mobile agents in vehicular networks

In the last years, the automotive industry has shown interest in the addition of computing and communication devices to cars, thanks to technological advances in these fields, in order to meet the increasing demand of “connected” applications and services. Although vehicular ad hoc networks (VANETs) have not been fully developed yet, they could be used in a near future as a means to provide a number of interesting applications and services that need the exchange of data among vehicles and other data sources. In this paper, we propose a spatial crowdsourcing schema for the opportunistic collection of information within an interest area in a city or region (e.g., measures about the environment, such as the concentration of certain gases in the atmosphere, or information such as the availability of parking spaces in an area), using vehicular ad hoc communications. We present a method that exploits mobile agent technology to accomplish the distributed collection and querying of data among vehicles in such a scenario. Our proposal is supported by an extensive set of realistic simulations that prove the feasibility of the approach

Game-Theoretic Safety Assurance for Human-Centered Robotic Systems

In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must have the ability to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads makes it unviable to rely on common design-time assumptions, since these may be violated once the system is deployed. Instead, the next generation of robotic technologies will need to reason about safety online, constructing high-confidence assurances informed by ongoing observations of the environment and other agents, in spite of models of them being necessarily fallible.This dissertation aims to lay down the necessary foundations to enable autonomous systems to ensure their own safety in complex, changing, and uncertain environments, by explicitly reasoning about the gap between their models and the real world. It first introduces a suite of novel robust optimal control formulations and algorithmic tools that permit tractable safety analysis in time-varying, multi-agent systems, as well as safe real-time robotic navigation in partially unknown environments; these approaches are demonstrated on large-scale unmanned air traffic simulation and physical quadrotor platforms. After this, it draws on Bayesian machine learning methods to translate model-based guarantees into high-confidence assurances, monitoring the reliability of predictive models in light of changing evidence about the physical system and surrounding agents. This principle is first applied to a general safety framework allowing the use of learning-based control (e.g. reinforcement learning) for safety-critical robotic systems such as drones, and then combined with insights from cognitive science and dynamic game theory to enable safe human-centered navigation and interaction; these techniques are showcased on physical quadrotors—flying in unmodeled wind and among human pedestrians—and simulated highway driving. The dissertation ends with a discussion of challenges and opportunities ahead, including the bridging of safety analysis and reinforcement learning and the need to ``close the loop'' around learning and adaptation in order to deploy increasingly advanced autonomous systems with confidence

Solving Multi-objective Integer Programs using Convex Preference Cones

Esta encuesta tiene dos objetivos: en primer lugar, identificar a los individuos que fueron víctimas de algún tipo de delito y la manera en que ocurrió el mismo. En segundo lugar, medir la eficacia de las distintas autoridades competentes una vez que los individuos denunciaron el delito que sufrieron. Adicionalmente la ENVEI busca indagar las percepciones que los ciudadanos tienen sobre las instituciones de justicia y el estado de derecho en Méxic

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity