Autonomous Mobile Robot with AI Based on Jetson Nano

Alexey, Gordeev; Klyachin, V. A.; Eldar, Kurbanov; Driaba, Aleksandr

doi:10.1007/978-3-030-63128-4_15

Cited by 6 publications

(1 citation statement)

References 5 publications

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“…An SBC consists of the central processing unit (CPU), random access memory (RAM), solid-state storage (SSS) and peripheral ports combined into a small form factor printed circuit board (PCB) 29,30,31,32,33 . The main two candidates for SBC were Nvidia Jetson Nano 4GB 34,35,36,37 and Raspberry Pi 4 Model B 8GB 38,39,40,41 . Nvidia Jetson Nano has a much more powerful Graphics processing unit (GPU), which makes it ideal for computer vision and machine learning tasks while the Raspberry Pi 4 Model B has the best overall computing power and a vast number of compatible modules and community support.…”

Section: Development Of the Ica Platformmentioning

confidence: 99%

An Intelligent Compaction Analyser: A versatile platform for real-time recording, monitoring and analysing of road material compaction

Ranasinghe

Sounthararajah

Kodikara

2022

Preprint

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This study aims to develop an innovative retrofittable platform with cutting-edge hardware and software tools to advance the current state of intelligent compaction technology. We built a prototype of this platform from the ground up, hereafter referred to as the Intelligent Compaction Analyser (ICA) platform. This platform is designed to provide the following novel features: [i] simultaneous visualisation of multiple intelligent compaction measurement values (ICMVs) in real-time during compaction; [ii] versatility to retrofit to an existing conventional roller; [iii] ability to incorporate corrections for different ICMV parameters reflecting variable field conditions; [iv] customization of construction specifications as needed for performance-based compaction; [v] ability to record key roller parameters such as speed, vibration frequency and amplitude for further analysis; and (vi) capability to monitor the raw vibration signal pattern and its frequency spectrum. In this paper, we provide the basic design concepts of this platform, outline its functionalities and capabilities, and provide initial field validation results. The results show that the ICA platform is effective in calculating conventional acceleration amplitude-based ICMVs and stiffness-based parameters, and in displaying the spatial distributions of these parameters in a colour-coded map in real-time during compaction.

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