Abstract
The autonomous robot has been successfully developed for industrial applications such as inspection. The gap is filled by making use of a robust machine vision algorithm for crack detection using ResNet-based deep learning technique. A novel navigation algorithm is fine-tuned for Firebird V, for its manoeuvres thus making this robot fully autonomous in its functionalities. It navigates in designated areas autonomously and identifies cracks or obstacles. The robot can be effectively used for structural health monitoring or in the case of a conveyor belt inspector. The developed algorithm can navigate on the track with the ability to avoid front collision and Zigzag motion; if any obstacle comes in front of the robot, the buzzer beeps to alert. This autonomous robot can be deployed in various industrial applications which makes use of fundamental concepts of our research. Our set-up for conveyor belt inspector and structural health monitoring requires further automation for repairs or obstacle removals. Further, the safety and reliability issues arising from the practical need to be addressed.
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Khalid, Faizabadi, A.R., Mallik, M.A. (2022). Machine Vision-Based Conveyor and Structural Health Monitoring Robot for Industrial Application Using Deep Learning. In: Reddy, A.B., Kiranmayee, B., Mukkamala, R.R., Srujan Raju, K. (eds) Proceedings of Second International Conference on Advances in Computer Engineering and Communication Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-7389-4_3
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DOI: https://doi.org/10.1007/978-981-16-7389-4_3
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