189 research outputs found
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
Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond
This paper presents a novel nonmyopic adaptive Gaussian process planning
(GPP) framework endowed with a general class of Lipschitz continuous reward
functions that can unify some active learning/sensing and Bayesian optimization
criteria and offer practitioners some flexibility to specify their desired
choices for defining new tasks/problems. In particular, it utilizes a
principled Bayesian sequential decision problem framework for jointly and
naturally optimizing the exploration-exploitation trade-off. In general, the
resulting induced GPP policy cannot be derived exactly due to an uncountable
set of candidate observations. A key contribution of our work here thus lies in
exploiting the Lipschitz continuity of the reward functions to solve for a
nonmyopic adaptive epsilon-optimal GPP (epsilon-GPP) policy. To plan in real
time, we further propose an asymptotically optimal, branch-and-bound anytime
variant of epsilon-GPP with performance guarantee. We empirically demonstrate
the effectiveness of our epsilon-GPP policy and its anytime variant in Bayesian
optimization and an energy harvesting task.Comment: 30th AAAI Conference on Artificial Intelligence (AAAI 2016), Extended
version with proofs, 17 page
ADMM-based Adaptive Sampling Strategy for Nonholonomic Mobile Robotic Sensor Networks
This paper discusses the adaptive sampling problem in a nonholonomic mobile
robotic sensor network for efficiently monitoring a spatial field. It is
proposed to employ Gaussian process to model a spatial phenomenon and predict
it at unmeasured positions, which enables the sampling optimization problem to
be formulated by the use of the log determinant of a predicted covariance
matrix at next sampling locations. The control, movement and nonholonomic
dynamics constraints of the mobile sensors are also considered in the adaptive
sampling optimization problem. In order to tackle the nonlinearity and
nonconvexity of the objective function in the optimization problem we first
exploit the linearized alternating direction method of multipliers (L-ADMM)
method that can effectively simplify the objective function, though it is
computationally expensive since a nonconvex problem needs to be solved exactly
in each iteration. We then propose a novel approach called the successive
convexified ADMM (SC-ADMM) that sequentially convexify the nonlinear dynamic
constraints so that the original optimization problem can be split into convex
subproblems. It is noted that both the L-ADMM algorithm and our SC-ADMM
approach can solve the sampling optimization problem in either a centralized or
a distributed manner. We validated the proposed approaches in 1000 experiments
in a synthetic environment with a real-world dataset, where the obtained
results suggest that both the L-ADMM and SC- ADMM techniques can provide good
accuracy for the monitoring purpose. However, our proposed SC-ADMM approach
computationally outperforms the L-ADMM counterpart, demonstrating its better
practicality.Comment: submitted to IEEE Sensors Journal, revised versio
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
Mobile networks and internet of things infrastructures to characterize smart human mobility
The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.This work has been supported by FCT–Fundação para a Ciência e Tecnologia
within the R&D Units Project Scope: UIDB/00319/2020. This work has also been supported by national funds through FCT–Fundação para a Ciência e Tecnologia through project UIDB/04728/202
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