Neuro-fuzzy based fusion of LiDAR and ultrasonic sensors to minimize error in range estimation for the navigation of mobile robots

Abstract

Multi sensor data fusion plays a significant role in addressing the research problems for mobile robot navigation. In this paper, a fusion procedure was developed, based on an Adaptive Neuro Fuzzy Inference Systems (ANFIS), which fuses the data from ultrasonic and LiDAR sensors for a better range estimation as well as environment perception. The root mean square error analysis between the measured and actual distance across different experiment trials indicates that, ultrasonic sensor could provide the data with a root mean square error (RMSE) which varies between 0.3118 cm and 4.9953 cm, whereas the LiDAR sensor could provide the same between 5.1503 cm and 10.4773 cm over 0–4 m range. The RMSE of the proposed fusion algorithm varies between 3.4831 cm and 6.1471 cm. It can be observed that, the fusion process could reduce the mean square error present with the high cost LiDAR sensing system by one-half, while fusing it with the low cost ultrasonic sensing system. The fusion algorithm discussed in this paper provides a guidance to define various operating/safety zones for initiating necessary control action during the navigation of the mobile robots.

Keywords

Adaptive neuro fuzzy inference systems data fusion LiDAR sensor ten-fold cross-validation ultrasonic sensor

Get full access to this article

View all access options for this article.

References

Klein

. Sensor and Data Fusion concepts and Applications. 2

{}^{\rm nd}

ed. Society of Photo-Optical Instrumentation Engineers (SPICE), USA; 1999.

Hall

Member

Llinas

. An Introduction to Multisensor Data Fusion. 1997; 85(1).

Khaleghi

Khamis

Karray

Razavi

. Multisensor data fusion: A review of the state-of-the-art. Inf Fusion [Internet].2013; 14(1): 28-44. Available from: 10.1016/j.inffus.2011.08.001.

Zadeh

. Fuzzy sets. Inf Control.1965; 8(3): 338-53.

Taghavifar

Mardani

. Fuzzy logic system based prediction effort: A case study on the effects of tire parameters on contact area and contact pressure. Appl Soft Comput J [Internet].2014; 14(PART C): 390-6. Available from: 10.1016/j.asoc.2013.10.005.

Bobyr

Titov

Kulabukhov

Syryamkin

. Two modular neuro-fuzzy system for mobile robot navigation. IOP Conf Ser Mater Sci Eng.2018; 363(1).

Yuan

Dong

Wang

. An adaptive fusion algorithm based on ANFIS for radar/infrared system. Expert Syst Appl [Internet].2009; 36(1): 111-20. Available from: 10.1016/j.eswa.2007.09.026.

Amudha

Radha

Smitha

. Analysis of fuzzy rule optimization models. Int J Eng Technol.2015; 7(5): 1564-70.

Majumder

Pratihar

. Multi-sensors data fusion through fuzzy clustering and predictive tools. Expert Syst Appl [Internet].2018; 107: 165-72. Available from: 10.1016/j.eswa.2018.04.026.

10.

Singh

Thongam

. Mobile Robot Navigation Using Fuzzy Logic in Static Environments. Procedia Comput Sci.2018; 125: 11-7.

11.

Mvemba

Guwa Gua Band

Lay-Ekuakille

Giannoccaro

. Advanced acoustic sensing system on a mobile robot: Design, construction and measurements. IEEE Instrum Meas Mag.2018; 21(2): 4-9.

12.

Fraichard

Garnier

. Fuzzy control to drive car-like vehicles. Rob Auton Syst.2001; 34(1): 1-22.

13.

Precup

Hellendoorn

. A survey on industrial applications of fuzzy control. Comput Ind [Internet].2011; 62(3): 213-26. Available from: 10.1016/j.compind.2010.10.001.

14.

Bosque

Del Campo

Echanobe

. Fuzzy systems, neural networks and neuro-fuzzy systems: A vision on their hardware implementation and platforms over two decades. Eng Appl Artif Intell [Internet].2014; 32(1): 283-331. Available from: 10.1016/j.engappai.2014.02.008.

15.

Zaki

Dunnigan

. A navigation strategy for an autonomous patrol vehicle based on multi-fusion planning algorithms and multi-paradigm representation schemes. Rob Auton Syst [Internet].2017; 96: 133-42. Available from: 10.1016/j.robot.2017.07.012.

16.

Mohanty

Parhi

. Navigation of an autonomous mobile robot using intelligent hybrid technique. 2012 IEEE Int Conf Adv Commun Control Comput Technol. 2012; (978): 136-40.

17.

Novak

Riener

. A survey of sensor fusion methods in wearable robotics. Rob Auton Syst [Internet].2015; 73: 155-70. Available from: 10.1016/j.robot.2014.08.012.

18.

Jang

JSR

. ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans Syst Man Cybern.1993; 23(3): 665-85.

19.

Al-mayyahi

Wang

Birch

. Adaptive Neuro-Fuzzy Technique for Autonomous Ground. 2014; 349-70.

20.

Nefti

Oussalah

Djouani

Pontnau

. Intelligent adaptive mobile robot navigation. J Intell Robot Syst Theory Appl.2001; 30(4): 311-29.

21.

Shihabudheen

Pillai

. Recent advances in neuro-fuzzy system: A survey. Knowledge-Based Syst [Internet].2018; 152: 136-62. Available from: 10.1016/j.knosys.2018.04.014.

22.

Demirli

Khoshnejad

. Autonomous parallel parking of a car-like mobile robot by a neuro-fuzzy sensor-based controller. Fuzzy Sets Syst [Internet].2009; 160(19): 2876-91. Available from: 10.1016/j.fss.2009.01.019.

23.

Saadeddin

Abdel-Hafez

Jaradat

Jarrah

. Performance enhancement of low-cost, high-accuracy, state estimation for vehicle collision prevention system using ANFIS. Mech Syst Signal Process [Internet].2013; 41(1–2): 239-53. Available from: 10.1016/j.ymssp.2013.06.013.

24.

Gandhi

Adarsh

Ramachandran

. Performance Analysis of Half Car Suspension Model with 4 DOF using PID, LQR, FUZZY and ANFIS Controllers. Procedia Comput Sci [Internet].2017; 115: 2-13. Available from: 10.1016/j.procs.2017.09.070.

25.

Kumar

Parhi

Kumar

. ANFIS approach for navigation of mobile robots. ARTCom 2009 – Int Conf Adv Recent Technol Commun Comput. 2009; 727-31.

26.

Iraji

. Multi-layer architecture for adaptive fuzzy inference system with a large number of input features. Cogn Syst Res [Internet].2017; 42: 23-41. Available from: 10.1016/j.cogsys.2016.11.006.

27.

Pham

Qiu

Aung

Wai

Biswas

. Application of Ultrasonic Sensors in a Smart Environment. 2007; 3: 180-207.

28.

Megalingam

Chacko

Kumar

Jacob

Gautham

. Gesture controlled wheel chair using IR-LED TSOP pairs along with collision avoidance. Int Conf Robot Autom Humanit Appl RAHA 2016 – Conf Proc. 2017; 4-10.

29.

Adarsh

Kaleemuddin

Bose

Ramachandran

. Performance comparison of Infrared and Ultrasonic sensors for obstacles of different materials in vehicle/ robot navigation applications. In: IOP Conference Series: Materials Science and Engineering, 2016.

30.

Nguyen

Gächter

Martinelli

Tomatis

Siegwart

. A comparison of line extraction algorithms using 2D range data for indoor mobile robotics. Auton Robots.2007; 23(2): 97-111.

31.

Yalcin

Sayar

Arar

Akpinar

Kosunalp

. Approaches of road boundary and obstacle detection using LIDAR. IFAC Proc Vol.2013; 1(PART1): 211-5.

32.

Malavazi

FBP

Guyonneau

Fasquel

Lagrange

Mercier

. LiDAR-only based navigation algorithm for an autonomous agricultural robot. Comput Electron Agric [Internet].2018; 154(August): 71-9. Available from: 10.1016/j.compag.2018.08.034.

33.

Zhao

Chai

Gao

. Obstacle avoidance and motion planning scheme for a hexapod robot Octopus-III. Rob Auton Syst [Internet].2018; 103: 199-212. Available from: 10.1016/j.robot.2018.01.007.

34.

Noureldin

El-Shafie

Reda Taha

. Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation. Eng Appl Artif Intell.2007; 20(1): 49-61.