摘要
This study proposes an efficient wall-following and navigation control model that includes three control modes, namely w all-f ollowing (WF), t oward-g oal (TG), and b ehavior m anager (BM). To achieve an adaptive controller for WF mode, an efficientr ecurrent f uzzy c erebellar m odel a rticulation c ontroller (RFCMAC) based on d ynamic g roup a rtificial b ee c olony (DGABC) is proposed for implementing reinforcement learning process. The fitness function includes four assessment factors which are defined as follows: (1) maintaining safe distance between the mobile robot and the wall; (2) ensuring successfully running a cycle; (3) avoiding mobile robot collisions; (4) mobile robot running at a maximum speed. Moreover, the BM is used to switch WF mode and TG mode, and is employed as an escape mechanism based on the relationship between the robot and the environment. The experimental results show that the proposed DGABC is more effective than the traditional ABC in WF mode. The proposed control method also obtains a better navigation control than other methods in unknown environments.
| 原文 | English |
|---|---|
| 頁（從 - 到） | 343-357 |
| 頁數 | 15 |
| 期刊 | Journal of Intelligent and Robotic Systems: Theory and Applications |
| 卷 | 92 |
| 發行號 | 2 |
| DOIs | |
| 出版狀態 | Published - 2018 十月 1 |
指紋
All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Mechanical Engineering
- Industrial and Manufacturing Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering
引用此文
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Wall-following and Navigation Control of Mobile Robot Using Reinforcement Learning Based on Dynamic Group Artificial Bee Colony. / Lin, Tzu Chao; Chen, Chao-Chun; Lin, Cheng Jian.
於: Journal of Intelligent and Robotic Systems: Theory and Applications, 卷 92, 編號 2, 01.10.2018, p. 343-357.研究成果: Article
TY - JOUR
T1 - Wall-following and Navigation Control of Mobile Robot Using Reinforcement Learning Based on Dynamic Group Artificial Bee Colony
AU - Lin, Tzu Chao
AU - Chen, Chao-Chun
AU - Lin, Cheng Jian
PY - 2018/10/1
Y1 - 2018/10/1
N2 - This study proposes an efficient wall-following and navigation control model that includes three control modes, namely w all-f ollowing (WF), t oward-g oal (TG), and b ehavior m anager (BM). To achieve an adaptive controller for WF mode, an efficientr ecurrent f uzzy c erebellar m odel a rticulation c ontroller (RFCMAC) based on d ynamic g roup a rtificial b ee c olony (DGABC) is proposed for implementing reinforcement learning process. The fitness function includes four assessment factors which are defined as follows: (1) maintaining safe distance between the mobile robot and the wall; (2) ensuring successfully running a cycle; (3) avoiding mobile robot collisions; (4) mobile robot running at a maximum speed. Moreover, the BM is used to switch WF mode and TG mode, and is employed as an escape mechanism based on the relationship between the robot and the environment. The experimental results show that the proposed DGABC is more effective than the traditional ABC in WF mode. The proposed control method also obtains a better navigation control than other methods in unknown environments.
AB - This study proposes an efficient wall-following and navigation control model that includes three control modes, namely w all-f ollowing (WF), t oward-g oal (TG), and b ehavior m anager (BM). To achieve an adaptive controller for WF mode, an efficientr ecurrent f uzzy c erebellar m odel a rticulation c ontroller (RFCMAC) based on d ynamic g roup a rtificial b ee c olony (DGABC) is proposed for implementing reinforcement learning process. The fitness function includes four assessment factors which are defined as follows: (1) maintaining safe distance between the mobile robot and the wall; (2) ensuring successfully running a cycle; (3) avoiding mobile robot collisions; (4) mobile robot running at a maximum speed. Moreover, the BM is used to switch WF mode and TG mode, and is employed as an escape mechanism based on the relationship between the robot and the environment. The experimental results show that the proposed DGABC is more effective than the traditional ABC in WF mode. The proposed control method also obtains a better navigation control than other methods in unknown environments.
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U2 - 10.1007/s10846-017-0743-y
DO - 10.1007/s10846-017-0743-y
M3 - Article
AN - SCOPUS:85036501170
VL - 92
SP - 343
EP - 357
JO - Journal of Intelligent and Robotic Systems: Theory and Applications
JF - Journal of Intelligent and Robotic Systems: Theory and Applications
SN - 0921-0296
IS - 2
ER -