loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Adam Milstein 1 and Tao Wang 2

Affiliations: 1 University of Waterloo, Canada ; 2 University of Alberta, Canada

ISBN: 978-972-8865-60-3

Keyword(s): Mobile Robots, Localization, Machine Learning.

Related Ontology Subjects/Areas/Topics: Autonomous Agents ; Informatics in Control, Automation and Robotics ; Mobile Robots and Autonomous Systems ; Robotics and Automation

Abstract: Localization is the problem of determining a robot’s location in an environment. Monte Carlo Localization (MCL) is a method of solving this problem by using a partially observable Markov decision process to find the robot’s state based on its sensor readings, given a static map of the environment. MCL requires a model of each sensor in order to work properly. One of the most important sensors involved is the estimation of the robot’s motion, based on its encoders that report what motion the robot has performed. Since these encoders are inaccurate, MCL involves using other sensors to correct the robot’s location. Usually, a motion model is created that predicts the robot’s actual motion, given a reported motion. The parameters of this model must be determined manually using exhaustive tests. Although an accurate motion model can be determined in advance, a single model cannot optimally represent a robot’s motion in all cases. With a terrestrial robot the ground surface, slope, motor we ar, and possibly tire inflation level will all alter the characteristics of the motion model. Thus, it is necessary to have a generalized model with enough error to compensate for all possible situations. However, if the localization algorithm is working properly, the result is a series of predicted motions, together with the corrections determined by the algorithm that alter the motions to the correct location. In this case, we demonstrate a technique to process these motions and corrections and dynamically determine revised motion parameters that more accurately reflect the robot’s motion. We also link these parameters to different locations so that area dependent conditions, such as surface changes, can be taken into account. These parameters might even be used to identify surface changes by examining the various parameters. By using the fact that MCL is working, we have improved the algorithm to adapt to changing conditions so as to handle even more complex situations. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.163.20.123

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Milstein A.; Wang T. and (2006). LOCALIZATION WITH DYNAMIC MOTION MODELS - Determining Motion Model Parameters Dynamically in Monte Carlo Localization.In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-972-8865-60-3, pages 120-127. DOI: 10.5220/0001204501200127

@conference{icinco06,
author={Adam Milstein and Tao Wang},
title={LOCALIZATION WITH DYNAMIC MOTION MODELS - Determining Motion Model Parameters Dynamically in Monte Carlo Localization},
booktitle={Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2006},
pages={120-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001204501200127},
isbn={978-972-8865-60-3},
}

TY - CONF

JO - Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - LOCALIZATION WITH DYNAMIC MOTION MODELS - Determining Motion Model Parameters Dynamically in Monte Carlo Localization
SN - 978-972-8865-60-3
AU - Milstein, A.
AU - Wang, T.
PY - 2006
SP - 120
EP - 127
DO - 10.5220/0001204501200127

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.