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Real-Time Robotic Search using Structural Spatial Point Processes
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-7248-1112
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.ORCID iD: 0000-0002-5115-5657
Kotte Consulting AB.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
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2020 (English)In: 35TH UNCERTAINTY IN ARTIFICIAL INTELLIGENCE CONFERENCE (UAI 2019), Association For Uncertainty in Artificial Intelligence (AUAI) , 2020, Vol. 115, p. 995-1005Conference paper, Published paper (Refereed)
Abstract [en]

Aerial robots hold great potential for aiding Search and Rescue (SAR) efforts over large areas, such as during natural disasters. Traditional approaches typically search an area exhaustively, thereby ignoring that the density of victims varies based on predictable factors, such as the terrain, population density and the type of disaster. We present a probabilistic model to automate SAR planning, with explicit minimization of the expected time to discovery. The proposed model is a spatial point process with three interacting spatial fields for i) the point patterns of persons in the area, ii) the probability of detecting persons and iii) the probability of injury. This structure allows inclusion of informative priors from e.g. geographic or cell phone traffic data, while falling back to latent Gaussian processes when priors are missing or inaccurate. To solve this problem in real-time, we propose a combination of fast approximate inference using Integrated Nested Laplace Approximation (INLA), and a novel Monte Carlo tree search tailored to the problem. Experiments using data simulated from real world Geographic Information System (GIS) maps show that the framework outperforms competing approaches, finding many more injured in the crucial first hours.

Place, publisher, year, edition, pages
Association For Uncertainty in Artificial Intelligence (AUAI) , 2020. Vol. 115, p. 995-1005
Series
Proceedings of Machine Learning Research (PMLR), E-ISSN 2640-3498 ; 115
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-159698ISI: 000722423500092Scopus ID: 2-s2.0-85084016675OAI: oai:DiVA.org:liu-159698DiVA, id: diva2:1343786
Conference
Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), Tel Aviv, Israel, July 22-25, 2019
Note

Funding: Wallenberg AI, Autonomous Systems and Software Program (WASP); WASP Autonomous Research Arenas - Knut and Alice Wallenberg Foundation; Swedish Foundation for Strategic Research (SSF)Swedish Foundation for Strategic Research; ELLIIT Excellence Center at Link opingLund for Information Technology

Available from: 2019-08-19 Created: 2019-08-19 Last updated: 2023-04-05Bibliographically approved
In thesis
1. Learning to Make Safe Real-Time Decisions Under Uncertainty for Autonomous Robots
Open this publication in new window or tab >>Learning to Make Safe Real-Time Decisions Under Uncertainty for Autonomous Robots
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Robots are increasingly expected to go beyond controlled environments in laboratories and factories, to act autonomously in real-world workplaces and public spaces. Autonomous robots navigating the real world have to contend with a great deal of uncertainty, which poses additional challenges. Uncertainty in the real world accrues from several sources. Some of it may originate from imperfect internal models of reality. Other uncertainty is inherent, a direct side effect of partial observability induced by sensor limitations and occlusions. Regardless of the source, the resulting decision problem is unfortunately computationally intractable under uncertainty. This poses a great challenge as the real world is also dynamic. It  will not pause while the robot computes a solution. Autonomous robots navigating among people, for example in traffic, need to be able to make split-second decisions. Uncertainty is therefore often neglected in practice, with potentially catastrophic consequences when something unexpected happens. The aim of this thesis is to leverage recent advances in machine learning to compute safe real-time approximations to decision-making under uncertainty for real-world robots. We explore a range of methods, from probabilistic to deep learning, as well as different combinations with optimization-based methods from robotics, planning and control. Driven by applications in robot navigation, and grounded in experiments with real autonomous quadcopters, we address several parts of this problem. From reducing uncertainty by learning better models, to directly approximating the decision problem itself, all the while attempting to satisfy both the safety and real-time requirements of real-world autonomy.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2020. p. 55
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2051
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-163419 (URN)10.3384/diss.diva-163419 (DOI)9789179298890 (ISBN)
Public defence
2020-04-29, Ada Lovelace, hus B, Linköpings Universitet, Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Swedish Foundation for Strategic Research ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2020-04-06 Created: 2020-03-26 Last updated: 2023-04-05Bibliographically approved
2. Scalable Bayesian spatial analysis with Gaussian Markov random fields
Open this publication in new window or tab >>Scalable Bayesian spatial analysis with Gaussian Markov random fields
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Skalbar Bayesiansk spatial analys med Gaussiska Markov-fält
Abstract [en]

Accurate statistical analysis of spatial data is important in many applications. Failing to properly account for spatial autocorrelation may often lead to false conclusions. At the same time, the ever-increasing sizes of spatial datasets pose a great computational challenge, as many standard methods for spatial analysis are limited to a few thousand data points.

In this thesis, we explore how Gaussian Markov random fields (GMRFs) can be used for scalable analysis of spatial data. GMRFs are closely connected to the commonly used Gaussian processes, but have sparsity properties that make them computationally cheap both in time and memory. The Bayesian framework enables a GMRF to be used as a spatial prior, comprising the assumption of smooth variation over space, and gives a principled way to estimate the parameters and propagate uncertainty.

We develop new algorithms that enable applying GMRF priors in 3D to the brain activity inherent in functional magnetic resonance imaging (fMRI) data, with millions of observations. We show that our methods are both faster and more accurate than previous work. A method for approximating selected elements of the inverse precision matrix (i.e. the covariance matrix) is also proposed, which is important for evaluating the posterior uncertainty. In addition, we establish a link between GMRFs and deep convolutional neural networks, which have been successfully used in countless machine learning tasks for images, resulting in a deep GMRF model. Finally, we show how GMRFs can be used in real-time robotic search and rescue operations, for modeling the spatial distribution of injured persons.

Abstract [sv]

Tillförlitlig statistisk analys av spatiala data är viktigt inom många tillämpningar. Om inte korrekt hänsyn tas till spatial autokorrelation kan det ofta leda till felaktiga slutsatser. Samtidigt ökar ständigt storleken på de spatiala datamaterialen vilket utgör en stor beräkningsmässig utmaning, eftersom många standardmetoder för spatial analys är begränsade till några tusental datapunkter.

I denna avhandling utforskar vi hur Gaussiska Markov-fält (eng: Gaussian Markov random fields, GMRF) kan användas för mer skalbara analyser av spatiala data. GMRF-modeller är nära besläktade med de ofta använda Gaussiska processerna, men har gleshetsegenskaper som gör dem beräkningsmässigt effektiva både vad gäller tids- och minnesåtgång. Det Bayesianska synsättet gör det möjligt att använda GMRF som en spatial prior som innefattar antagandet om långsam spatial variation och ger ett principiellt tillvägagångssätt för att skatta parametrar och propagera osäkerhet.

Vi utvecklar nya algoritmer som gör det möjligt att använda GMRF-priors i 3D för den hjärnaktivitet som indirekt kan observeras i hjärnbilder framtagna med tekniken fMRI, som innehåller milliontals datapunkter. Vi visar att våra metoder är både snabbare och mer korrekta än tidigare forskning. En metod för att approximera utvalda element i den inversa precisionsmatrisen (dvs. kovariansmatrisen) framförs också, vilket är viktigt för att kunna evaluera osäkerheten i posteriorn. Vidare gör vi en koppling mellan GMRF och djupa neurala faltningsnätverk, som har använts framgångsrikt för mängder av bildrelaterade problem inom maskininlärning, vilket mynnar ut i en djup GMRF-modell. Slutligen visar vi hur GMRF kan användas i realtid av autonoma drönare för räddningsinsatser i katastrofområden för att modellera den spatiala fördelningen av skadade personer.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2020. p. 53
Series
Linköping Studies in Arts and Sciences, ISSN 0282-9800 ; 790Linköping Studies in Statistics, ISSN 1651-1700 ; 15
Keywords
Spatial statistics, Bayesian statistics, Gaussian Markov random fields, fMRI, Machine learning, Spatial statistik, Bayesiansk statistik, Gaussiska Markov-fält, fMRI, Maskininlärning
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-165872 (URN)10.3384/diss.diva-165872 (DOI)9789179298180 (ISBN)
Public defence
2020-09-18, Ada Lovelace, B Building, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2020-08-17 Created: 2020-06-01 Last updated: 2020-11-24Bibliographically approved

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Scopushttp://auai.org/uai2019/proceedings/papers/368.pdf

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Andersson, OlovSidén, PerDoherty, PatrickVillani, Mattias

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