Publication Type : Journal Article
Publisher : Automatic Control and Computer Sciences
Source : Automatic Control and Computer Sciences, Pleiades Publishing, Volume 52, Number 5, p.382-391 (2018)
Keywords : ANFIS, Autonomous Mobile Robot, Data Fusion, Environment perceptions, Error analysis, Errors, Fuzzy, Fuzzy inference, Fuzzy neural networks, Infrared detectors, IR sensor, Machine design, Mean absolute percentage error, Mean square error, Membership functions, Mobile Robot Navigation, Mobile robots, Navigation, Navigation systems, Operations research, Root mean square errors, Sensor data fusion, Ultrasonic sensors
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Mechanical Engineering
Year : 2018
Abstract :
Abstract: Design and development of autonomous mobile robots attracts more attention in the era of autonomous navigation. There are various algorithms used in practice for solving research problems related to the robot model and its operating environment. This paper presents the design of data fusion algorithm using Adaptive Neuro Fuzzy Interface (ANFIS) for the navigation of mobile robots. Detailed analysis of various membership functions (MFs) provided in this paper helps to select the most appropriate MF for the design of similar navigation systems. The combined use of fuzzy and neural networks in ANFIS makes the measured distance value of the residual covariance consistent with its actual value. The data fusion algorithm within the controller of the mobile robot fuses the input from ultrasonic and infrared sensors for better environment perception. The results indicate that the data fusion algorithm provides minimal root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared with that of the individual sensors. © 2018, Allerton Press, Inc.
Cite this Research Publication : S. Adarsh and Dr. K. I. Ramachandran, “Design of Sensor Data Fusion Algorithm for Mobile Robot Navigation Using ANFIS and Its Analysis Across the Membership Functions”, Automatic Control and Computer Sciences, vol. 52, pp. 382-391, 2018.