CLC number: TP242.6
On-line Access: 2019-03-11
Received: 2017-03-25
Revision Accepted: 2018-01-25
Crosschecked: 2019-02-15
Cited: 0
Clicked: 6882
Panati Subbash, Kil To Chong. Adaptive network fuzzy inference system based navigation controller for mobile robot[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.1700206 @article{title="Adaptive network fuzzy inference system based navigation controller for mobile robot", %0 Journal Article TY - JOUR
基于自适应网络模糊推理系统的移动机器人导航控制器关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]Algabri M, Mathkour H, Ramdane H, et al., 2015. Comparative study of soft computing techniques for mobile robot navigation in an unknown environment. Comput Human Behav, 50:42-56. [2]Ali AH, Shamshirband S, Anuar NB, et al., 2014. DFCL: dynamic fuzzy logic controller for intrusion detection. Facta Univ Ser Mech Eng, 12(2):183-193. [3]Al-Sagban, Dhaouadi R, 2016. Neural based autonomous navigation of wheeled mobile robots. J Autom Mob Robot Intell Syst, 10(2):64-72. [4]Badii A, Khan A, Raval R, et al., 2014. Situation assessment through multi-modal sensing of dynamic environments to support cognitive robot control. Facta Univ Ser Mech Eng, 12(3):251-260. [5]Faisal M, Hedjar R, Al Sulaiman M, et al., 2013. Fuzzy logic navigation and obstacle avoidance by a mobile robot in an unknown dynamic environment. Int J Adv Robot Syst, 10:37. [6]Gudarzi M, 2016. Reliable robust controller for half-car active suspension systems based on human-body dynamics. Facta Univ Ser Mech Eng, 14(2):121-134. [7]Guo Y, Qu ZH, Wang J, 2003. A new performance-based motion planner for nonholonomic mobile robots. Proc 3rd Performance Metrics for Intelligent Systems Workshop, p.1-8. [8]Jang JSR, 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern, 23(3):665- 685. [9]Kim CJ, Chwa D, 2015. Obstacle avoidance method for wheeled mobile robots using interval type-2 fuzzy neural network. IEEE Trans Fuzzy Syst, 23(3):677-687. [10]Kundu S, Parhi DR, Deepak BBVL, 2012. Fuzzy-neuro based navigational strategy for mobile robot. Int J Sci Eng Res, 3(6):97-102. [11]Kyrarini M, Slavnić S, Ristić-Durrant D, 2014. Fuzzy controller for the control of the mobile platform of the CORBYS robotic gait rehabilitation system. Facta Univ Ser Mech Eng, 12(3):223-234. [12]Li X, Choi BJ, 2013. Design of obstacle avoidance system for mobile robot using fuzzy logic systems. Int J Smart Home, 7(3):321-328. [13]Luo CM, Gao JY, Li XD, et al., 2014. Sensor-based autonomous robot navigation under unknown environments with grid map representation. Proc IEEE Symp on Swarm Intelligence, p.1-7. [14]Mohanty PK, Parhi DR, 2014. A new intelligent motion planning for mobile robot navigation using multiple adaptive neuro-fuzzy inference system. Appl Math Inform Sci, 8(5): 2527-2535. [15]Muñoz ND, Valencia JA, Londono N, 2007. Evaluation of navigation of an autonomous mobile robot. Proc Workshop on Performance Metrics for Intelligent Systems, p.15-21. [16]Oveisi A, Nestorović T, 2014. Robust mixed H2/H∞ active vibration controller in attenuation of smart beam. Facta Univ Ser Mech Eng, 12(3):235-249. [17]Petković D, Gocić M, Shamshirband S, 2016. Adaptive neuro- fuzzy computing technique for precipitation estimation. Facta Univ Ser Mech Eng, 14(2):209-218. [18]Rosenblatt J, 1997. DAMN: a Distributed Architecture for Mobile Navigation. PhD Thesis, the Robotics Institute, Carnegie Mellon University, Pittsburgh, USA. [19]Rusu CG, Birou IT, 2010. Obstacle avoidance fuzzy system for mobile robot with IR sensors. Proc 10th Int Conf on Development and Application Systems, p.22. Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE |
Open peer comments: Debate/Discuss/Question/Opinion
<1>