Autonomous Mobile Robot Control in Unknown Environment with a Simple Map

In autonomous movement using Simultaneous Localization and Mapping(SLAM), it is necessary to build a detailed map of the environment in advance to set destinations and estimate the robot’s self-position. This requirement poses an obstacle to performing autonomous movement in locations the robot is visiting for the first time. In this study, we reduce the effort by using a simplified map built beforehand. Because the pre-built map is not detailed, transformations are performed by matching with the environment. In addition, destinations given in the simplified map are converted to the actual environment. Even when movement was deemed impossible because of obstacles during the transformation, it was confirmed that movement could continue by adding or changing the position of the destination. Simulations and experiments were conducted to confirm that the robot passed through all destinations. Thus, using this method, the robot can navigate the environment it is visiting for the first time.

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Publication Details

Published
2024-11-03
DOI
https://doi.org/10.1109/iecon55916.2024.10905755
Primary Topic
Robotic Path Planning Algorithms
Type
article
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article

Autonomous Mobile Robot Control in Unknown Environment with a Simple Map

Yasutaka Fujimoto, Sota Ushigome
Robotic Path Planning Algorithms
article

Autonomous Mobile Robot Control in Unknown Environment with a Simple Map

Yasutaka Fujimoto, Sota Ushigome
article en

Abstract

In autonomous movement using Simultaneous Localization and Mapping(SLAM), it is necessary to build a detailed map of the environment in advance to set destinations and estimate the robot’s self-position. This requirement poses an obstacle to performing autonomous movement in locations the robot is visiting for the first time. In this study, we reduce the effort by using a simplified map built beforehand. Because the pre-built map is not detailed, transformations are performed by matching with the environment. In addition, destinations given in the simplified map are converted to the actual environment. Even when movement was deemed impossible because of obstacles during the transformation, it was confirmed that movement could continue by adding or changing the position of the destination. Simulations and experiments were conducted to confirm that the robot passed through all destinations. Thus, using this method, the robot can navigate the environment it is visiting for the first time.

Yokohama National University (JP)
Openalex Percentile: Top 72%
Robotic Path Planning Algorithms
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