Robust mobile robot navigation in cluttered environments based on hybrid adaptive neuro-fuzzy inference and sensor fusion
Haider, Muhammad Husnain4,5; Wang, Zhonglai4,5; Khan, Abdullah Aman1,4; Ali, Hub2; Zheng, Hao4; Usman, Shaban4; Kumar, Rajesh4,5; Bhutta, M. Usman Maqbool3; Zhi, Pengpeng4,5
刊名JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
2022-11-01
卷号34期号:10页码:9060-9070
关键词ANFIS GPS Mobile robot Obstacle avoidance Autonomous navigation
ISSN号1319-1578
DOI10.1016/j.jksuci.2022.08.031
通讯作者Wang, Zhonglai(wzhonglai@uestc.edu.cn) ; Zhi, Pengpeng(zhipeng17@yeah.net)
英文摘要Collision-free navigation of mobile robots is a challenging task, especially in unknown environments, and various studies have been carried out in this regard. However, the previous studies have shortcomings, such as low performance in cluttered and unknown environments, high computational costs, and multiple controller models for navigation. This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) and global positioning system (GPS) for control and navigation to overcome these problems. The proposed method automates the navigation of a mobile robot while averting obstacles in unknown and densely cluttered environments. Furthermore, the mobile robots' global path planning and steering are controlled using GPS and heading sensor data fusion to achieve the target coordinates. A fuzzy inference system (FIS) is adopted to model obstacle avoidance where distance sensors data is converted into fuzzy linguistics. Moreover, a type-1 Takagi-Sugeno FIS is used to train a five-layered neural network for the local planning of the robot, and ANFIS parameters are tuned using a hybrid learning method. In addition, an algorithm is designed to generate a dataset for testing and training the ANFIS controller. All the testing and training are conducted in MATLAB, while simulations are carried out using CoppeliaSim. Comprehensive experiments are performed to validate the robustness of the proposed method. The results of the experiments show that the proposed approach outperforms various state-of-the-art neuro-fuzzy, CS-ANFIS, multi-ANFIS, and hybrid ANFIS navigation and obstacle avoidance methods in finding a near-optimal path in unknown environments.
资助项目Sichuan Science and Technology Program[2020JDJQ0036] ; Natural Science Foundation of Sichuan Province[2022NSFSC1941] ; Natural Science Foundation of Sichuan Province[2022]
WOS关键词OBSTACLE AVOIDANCE ; ALGORITHM ; IDENTIFICATION ; CONTROLLER
WOS研究方向Computer Science
语种英语
出版者ELSEVIER
WOS记录号WOS:000999620800010
资助机构Sichuan Science and Technology Program ; Natural Science Foundation of Sichuan Province
内容类型期刊论文
源URL[http://ir.ia.ac.cn/handle/173211/53600]  
专题多模态人工智能系统全国重点实验室
通讯作者Wang, Zhonglai; Zhi, Pengpeng
作者单位1.Sichuan Artificial Intelligence Res Inst, Yibin 644000, Sichuan, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong 999077, Peoples R China
4.Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 64000, Sichuan, Peoples R China
5.Univ Elect Sci & Technol China, Yangtze Delta Reg Inst, Huzhou 313001, Zhejiang, Peoples R China
推荐引用方式
GB/T 7714
Haider, Muhammad Husnain,Wang, Zhonglai,Khan, Abdullah Aman,et al. Robust mobile robot navigation in cluttered environments based on hybrid adaptive neuro-fuzzy inference and sensor fusion[J]. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES,2022,34(10):9060-9070.
APA Haider, Muhammad Husnain.,Wang, Zhonglai.,Khan, Abdullah Aman.,Ali, Hub.,Zheng, Hao.,...&Zhi, Pengpeng.(2022).Robust mobile robot navigation in cluttered environments based on hybrid adaptive neuro-fuzzy inference and sensor fusion.JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES,34(10),9060-9070.
MLA Haider, Muhammad Husnain,et al."Robust mobile robot navigation in cluttered environments based on hybrid adaptive neuro-fuzzy inference and sensor fusion".JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 34.10(2022):9060-9070.
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