TY - GEN
T1 - Autonomous Cross-Floor Navigation System for a ROS-Based Modular Service Robot
AU - Wang, Wen Hsin
AU - Chien, Yi Hsing
AU - Chiang, Hsin Han
AU - Wang, Wei Yen
AU - Hsu, Chen Chien
N1 - Funding Information:
This work was financially supported by the “Chinese Language and Technology Center” of National Taiwan
Funding Information:
Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, and Ministry of Science and Technology, Taiwan, under Grants no. MOST 108-2634-F-003-002 and MOST 108-2634-F-003-003 through Pervasive Artificial Intelligence Research (PAIR) Labs. We are grateful to the National Center for High-performance Computing for computer time and facilities to conduct this research.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - In this paper, we present an autonomous cross-floor navigation system including mapping, localization, path planning, and scene recognition based on robot operating system (ROS) architecture. The Gmapping algorithm is utilized to build a 2D map with a laser range-finder, and AMCL algorithm is utilized in the robot localization. Moreover, an improved A∗ algorithm is proposed to prevent robot from getting too close to the wall. Because our robot needs to navigate in the multi-floor environment, a decision system using deep convolutional neural network (DCNN) is also designed to recognize the current floor and the associated map can be download to the robot system. By training with the scene images of the featured location in each floor, the robot can recognize the current floor and then complete the navigation task. Finally, real test of our robot is conducted to demonstrate the feasibility of the proposed method.
AB - In this paper, we present an autonomous cross-floor navigation system including mapping, localization, path planning, and scene recognition based on robot operating system (ROS) architecture. The Gmapping algorithm is utilized to build a 2D map with a laser range-finder, and AMCL algorithm is utilized in the robot localization. Moreover, an improved A∗ algorithm is proposed to prevent robot from getting too close to the wall. Because our robot needs to navigate in the multi-floor environment, a decision system using deep convolutional neural network (DCNN) is also designed to recognize the current floor and the associated map can be download to the robot system. By training with the scene images of the featured location in each floor, the robot can recognize the current floor and then complete the navigation task. Finally, real test of our robot is conducted to demonstrate the feasibility of the proposed method.
KW - Cross-floor navigation system
KW - Deep convolutional neural network (DCNN)
KW - Modular service robot
KW - ROS architecture
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U2 - 10.1109/ICMLC48188.2019.8949176
DO - 10.1109/ICMLC48188.2019.8949176
M3 - Conference contribution
AN - SCOPUS:85078531795
T3 - Proceedings - International Conference on Machine Learning and Cybernetics
BT - Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019
PB - IEEE Computer Society
T2 - 18th International Conference on Machine Learning and Cybernetics, ICMLC 2019
Y2 - 7 July 2019 through 10 July 2019
ER -