A novel method for vehicular network system using static QR code for bituminous roads

Mittal, Rohit; Pathak, Vibhakar; Saini, G. L.; Raja, Linesh

doi:10.47974/JDMSC-1981

Open Access Research Article

A novel method for vehicular network system using static QR code for bituminous roads

Rohit Mittalrohit18mittal@yahoo.comDepartment of IoT and Intelligent SystemsManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Vibhakar Pathakvibhakarp@rediffmail.comDepartment of Information TechnologyArya College of Engineering & ITJaipur, Rajasthan, 3032028, IndiaView full profile → , ^*G. L. SainiCorresponding authorglsaini86@gmail.comDepartment of IoT and Intelligent SystemsManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Linesh Rajalinesh.raja@jaipur.manipal.eduDepartment of Computer ApplicationsManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →

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pp. 1271–1281Vol. 27Issue 4June 2024DOI: 10.47974/JDMSC-1981 Crossmark XML

Published Online:: 26 Jun 2024
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1981
Pages:: 1271–1281

Abstract

The simultaneous localization and mapping approach is used to predict a robot’s location as there is a concern in predicting a robot’s movement in lane environment. Simultaneous Localization and Mapping (SLAM) and Detection of Moving and Non-Moving Objects in Wireless Sensor Network (WSN) are the two main tasks involved in perception for detection of objects. The robot may generate a map of its surroundings while being localised at the same time through SLAM using sensor data. An analysis on Extended Kalman Filter- (EKF) and Unscented Kalman Filter (UKF) SLAM and EKF SLAM with WSN data augmentation for path prediction & correction for autonomous vehicle is presented. Improvised Active Edge Table (AET) algorithm based on above analysis is proposed. In this paper, the detection of obstacles is done through edge points. The experimented environment for route planning is the bituminous lane system road. Robot route planning is based on Quick Response (QR) data collected from WSN system and obstacle mounted QR system. The authors analysed the behaviour and movement of robot by comparing the route path by applying UKF and EKF SLAM algorithms. We conclude that while implementing UKF algorithm the probability for object collision with robot is 31%, whereas if EKF- SLAM with WSN algorithm is applied the probability for object collision is 25%.

Keywords

Simultaneous localization and mapping Extended Kalman filter Unscented Kalman filter Active edge table QR

Subject Classifications

68M25

References

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