@inproceedings{95b8ff5868b746bf8cfd6e1e9406231d,
title = "Bili{\c s}sel robotlar i{\c c}in eylem y{\"u}r{\"u}tme hatalarinin tanisi",
abstract = "Execution failures are likely in robotic applications due to dynamic and partially observable structure of the physical world. These failures should be detected by the robot, and a reasoning procedure should take place to diagnose the causes of the failures. In this paper, we propose a Hierarchical Hidden Markov Model (HHMM) based failure diagnosis method to identify the cause of a failure. Parallel HHMMs are used in the proposed method in order to track different type of failures. The performance of the proposed method is evaluated on our Pioneer 3-AT robot in several failure scenarios. The results reveal that using a probabilistic method ensures diagnosing multiple failures when there are more than one cause of a failure. Furthermore, using relations between the failure types and actions decreases memory requirements of the method by reducing the state space.",
keywords = "Failure Isolation for Robots, Hierarchical Hidden Markov Model, Model-Based Diagnosis",
author = "Dogan Altan and Sanem Sariel",
year = "2014",
doi = "10.1109/SIU.2014.6830540",
language = "T{\"u}rk{\c c}e",
isbn = "9781479948741",
series = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings",
publisher = "IEEE Computer Society",
pages = "1559--1562",
booktitle = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings",
address = "United States",
note = "2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 ; Conference date: 23-04-2014 Through 25-04-2014",
}