The local minimum escape using the grid map

Mook Jeong Sonn; Houn Song Tae; Hwan Park Ji; Wook Jeon Jae

doi:10.1109/MFI.2008.4648095

The local minimum escape using the grid map

Mook Jeong Sonn, Houn Song Tae, Hwan Park Ji, Wook Jeon Jae

Department of Semiconductor Systems Engineering

Sungkyunkwan University

Research output: Contribution to conference › Paper › peer-review

5 Scopus citations

Abstract

Our study is concerned with avoiding the local minimum that occurs when the mobile robot is trapped at a concave-shaped obstacle. Mobile robots use VFF (Virtual Force Field) as their autonomous navigation method. This algorithm generates the attractive force from the target and the repulsive force from the obstacle. So using the sum of these forces, the mobile robot moves to the target point. The method to avoid a local minimum has two steps. The first step is that the mobile robot detects being trapped by an obstacle. The second step is that the mobile robot escapes from the trap point. Then the mobile robot can move to the target point. To execute this, our study uses a grid map to represent the robot's surroundings. The map is constructed by the robot sensor.

Original language	English
Pages	378-383
Number of pages	6
DOIs	https://doi.org/10.1109/MFI.2008.4648095
State	Published - 2008
Event	2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI - Seoul, Korea, Republic of Duration: 20 Aug 2008 → 22 Aug 2008

Conference

Conference	2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI
Country/Territory	Korea, Republic of
City	Seoul
Period	20/08/08 → 22/08/08

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10.1109/MFI.2008.4648095

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title = "The local minimum escape using the grid map",

abstract = "Our study is concerned with avoiding the local minimum that occurs when the mobile robot is trapped at a concave-shaped obstacle. Mobile robots use VFF (Virtual Force Field) as their autonomous navigation method. This algorithm generates the attractive force from the target and the repulsive force from the obstacle. So using the sum of these forces, the mobile robot moves to the target point. The method to avoid a local minimum has two steps. The first step is that the mobile robot detects being trapped by an obstacle. The second step is that the mobile robot escapes from the trap point. Then the mobile robot can move to the target point. To execute this, our study uses a grid map to represent the robot's surroundings. The map is constructed by the robot sensor.",

author = "Sonn, \{Mook Jeong\} and Tae, \{Houn Song\} and Ji, \{Hwan Park\} and Jae, \{Wook Jeon\}",

year = "2008",

doi = "10.1109/MFI.2008.4648095",

language = "English",

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note = "2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI ; Conference date: 20-08-2008 Through 22-08-2008",

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AU - Sonn, Mook Jeong

AU - Tae, Houn Song

AU - Ji, Hwan Park

AU - Jae, Wook Jeon

PY - 2008

Y1 - 2008

N2 - Our study is concerned with avoiding the local minimum that occurs when the mobile robot is trapped at a concave-shaped obstacle. Mobile robots use VFF (Virtual Force Field) as their autonomous navigation method. This algorithm generates the attractive force from the target and the repulsive force from the obstacle. So using the sum of these forces, the mobile robot moves to the target point. The method to avoid a local minimum has two steps. The first step is that the mobile robot detects being trapped by an obstacle. The second step is that the mobile robot escapes from the trap point. Then the mobile robot can move to the target point. To execute this, our study uses a grid map to represent the robot's surroundings. The map is constructed by the robot sensor.

AB - Our study is concerned with avoiding the local minimum that occurs when the mobile robot is trapped at a concave-shaped obstacle. Mobile robots use VFF (Virtual Force Field) as their autonomous navigation method. This algorithm generates the attractive force from the target and the repulsive force from the obstacle. So using the sum of these forces, the mobile robot moves to the target point. The method to avoid a local minimum has two steps. The first step is that the mobile robot detects being trapped by an obstacle. The second step is that the mobile robot escapes from the trap point. Then the mobile robot can move to the target point. To execute this, our study uses a grid map to represent the robot's surroundings. The map is constructed by the robot sensor.

UR - https://www.scopus.com/pages/publications/67650514842

U2 - 10.1109/MFI.2008.4648095

DO - 10.1109/MFI.2008.4648095

M3 - Paper

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EP - 383

T2 - 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI

Y2 - 20 August 2008 through 22 August 2008

ER -

The local minimum escape using the grid map

Abstract

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