Applications of Probabilistic Graphical Models to Diagnosis and Control of Autonomous Vehicles

Anders L. Madsen; Uffe Bro Kjærulff; Jörg Kalwa; Michel Perrier; Miguel Angel Sotelo

Applications of Probabilistic Graphical Models to Diagnosis and Control of Autonomous Vehicles

Anders L. Madsen, Uffe Bro Kjærulff, Jörg Kalwa, Michel Perrier, Miguel Angel Sotelo

Department of Computer Science

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Abstract

We present the main elements of a distributed architecture supporting diagnosis and control of autonomous robots. The purpose of the architecture is to assist the operator or piloting system in managing fault detection, risk assessment, and recovery plans under uncertainty. The architecture is generic, open, and modular consisting of a set of interacting modules including a decision module (DM) and a set of intelligent modules (IMs). The DM communicates with the IMs to request and obtain diagnosis and recovery action proposals based on data obtained from the robot piloting module. The architecture supports the use of multiple artificial intelligence techniques collaborating on the task of handling uncertainty.

Original language	English
Title of host publication	The Second Bayesian Modeling Applications Workshop
Publication date	2004
Publication status	Published - 2004
Event	20th Conference on Uncertainty in Artificial Intelligence - Banff Park Lodge, Banff, Canada Duration: 7 Jul 2004 → 11 Jul 2004 Conference number: 20th

Conference

Conference	20th Conference on Uncertainty in Artificial Intelligence
Number	20th
Country/Territory	Canada
City	Banff Park Lodge, Banff
Period	07/07/2004 → 11/07/2004

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@inproceedings{bfe113f0bc0c11dbbbec000ea68e967b,

title = "Applications of Probabilistic Graphical Models to Diagnosis and Control of Autonomous Vehicles",

abstract = "We present the main elements of a distributed architecture supporting diagnosis and control of autonomous robots. The purpose of the architecture is to assist the operator or piloting system in managing fault detection, risk assessment, and recovery plans under uncertainty. The architecture is generic, open, and modular consisting of a set of interacting modules including a decision module (DM) and a set of intelligent modules (IMs). The DM communicates with the IMs to request and obtain diagnosis and recovery action proposals based on data obtained from the robot piloting module. The architecture supports the use of multiple artificial intelligence techniques collaborating on the task of handling uncertainty.",

author = "Madsen, {Anders L.} and Kj{\ae}rulff, {Uffe Bro} and J{\"o}rg Kalwa and Michel Perrier and Sotelo, {Miguel Angel}",

year = "2004",

language = "English",

booktitle = "The Second Bayesian Modeling Applications Workshop",

note = "20th Conference on Uncertainty in Artificial Intelligence ; Conference date: 07-07-2004 Through 11-07-2004",

}

TY - GEN

T1 - Applications of Probabilistic Graphical Models to Diagnosis and Control of Autonomous Vehicles

AU - Madsen, Anders L.

AU - Kjærulff, Uffe Bro

AU - Kalwa, Jörg

AU - Perrier, Michel

AU - Sotelo, Miguel Angel

N1 - Conference code: 20th

PY - 2004

Y1 - 2004

N2 - We present the main elements of a distributed architecture supporting diagnosis and control of autonomous robots. The purpose of the architecture is to assist the operator or piloting system in managing fault detection, risk assessment, and recovery plans under uncertainty. The architecture is generic, open, and modular consisting of a set of interacting modules including a decision module (DM) and a set of intelligent modules (IMs). The DM communicates with the IMs to request and obtain diagnosis and recovery action proposals based on data obtained from the robot piloting module. The architecture supports the use of multiple artificial intelligence techniques collaborating on the task of handling uncertainty.

AB - We present the main elements of a distributed architecture supporting diagnosis and control of autonomous robots. The purpose of the architecture is to assist the operator or piloting system in managing fault detection, risk assessment, and recovery plans under uncertainty. The architecture is generic, open, and modular consisting of a set of interacting modules including a decision module (DM) and a set of intelligent modules (IMs). The DM communicates with the IMs to request and obtain diagnosis and recovery action proposals based on data obtained from the robot piloting module. The architecture supports the use of multiple artificial intelligence techniques collaborating on the task of handling uncertainty.

M3 - Article in proceeding

BT - The Second Bayesian Modeling Applications Workshop

T2 - 20th Conference on Uncertainty in Artificial Intelligence

Y2 - 7 July 2004 through 11 July 2004

ER -

Applications of Probabilistic Graphical Models to Diagnosis and Control of Autonomous Vehicles

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Conference

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