Abstract
Autonomous navigation of a mobile robot in an unknown environment with highly cluttered obstacles is a fundamental issue in mobile robotics research. We propose an adaptive network fuzzy inference system (ANFIS) based navigation controller for a differential drive mobile robot in an unknown environment with cluttered obstacles. Ultrasonic sensors are used to capture the environmental information around the mobile robot. A training data set required to train the ANFIS controller has been obtained by designing a fuzzy logic based navigation controller. Additive white Gaussian noise has been added to the sensor readings and fed to the trained ANFIS controller during mobile robot navigation, to account for the effect of environmental noise on sensor readings. The robustness of the proposed navigation controller has been evaluated by navigating the mobile robot in three different environments. The performance of the proposed controller has been verified by comparing the travelled path length/efficiency and bending energy obtained by the proposed method with reference mobile robot navigation controllers, such as neural network, fuzzy logic, and ANFIS. Simulation results presented in this paper show that the proposed controller has better performance compared with reference controllers and can successfully navigate in different environments without any collision with obstacles.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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. https://doi.org/10.1016/j.chb.2015.03.062
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.
Al-Sagban, Dhaouadi R, 2016. Neural based autonomous navigation of wheeled mobile robots. J Autom Mob Robot Intell Syst, 10(2):64–72. https://doi.org/10.14313/JAMRIS_2–2016/17
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. https://doi.org/10.5281/zenodo.55392
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. https://doi.org/10.5772/54427
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. https://doi.org/10.22190/FUME1602121G
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.
Jang JSR, 1993. ANFIS: adaptive–network–based fuzzy inference system. IEEE Trans Syst Man Cybern, 23(3):665–685. https://doi.org/10.1109/21.256541
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. https://doi.org/10.1109/TFUZZ.2014.2321771
Kundu S, Parhi DR, Deepak BBVL, 2012. Fuzzy–neuro based navigational strategy for mobile robot. Int J Sci Eng Res, 3(6):97–102.
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. https://doi.org/10.5281/zenodo.55394
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.
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. https://doi.org/10.1109/SIS.2014.7011782
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. https://doi.org/10.12785/amis/080551
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. https://doi.org/10.1145/1660877.1660878
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.
Petković D, Gocić M, Shamshirband S, 2016. Adaptive neurofuzzy computing technique for precipitation estimation. Facta Univ Ser Mech Eng, 14(2):209–218. https://doi.org/10.22190/FUME1602209P
Rosenblatt J, 1997. DAMN: a Distributed Architecture for Mobile Navigation. PhD Thesis, the Robotics Institute, Carnegie Mellon University, Pittsburgh, USA.
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Project supported by the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, and Future Planning (No. NRF-2017M3C7A1044815) and Research Base Construction Fund Support Program funded by Chonbuk National University in 2015
Rights and permissions
About this article
Cite this article
Subbash, P., Chong, K.T. Adaptive network fuzzy inference system based navigation controller for mobile robot. Frontiers Inf Technol Electronic Eng 20, 141–151 (2019). https://doi.org/10.1631/FITEE.1700206
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.1700206
Key words
- Adaptive network fuzzy inference system
- Additive white Gaussian noise
- Autonomous navigation
- Mobile robot