An Affordable Six-Wheel Arduino Rover for Hands-On Learning in Planetary Robotics and Environmental Sensing with A Path Toward Edge AI

This work reviews recent literature (2021-2026) on low-cost rover platforms, educational robotics, six-wheel mobility, environmental sensing, and embedded machine learning, and positions an affordable six-wheel Arduino rover within this landscape.The platform pairs an Arduino Uno with six DC geared motors driven through an L298N dual H-bridge, while an HC-05 Bluetooth module links the chassis to an Android handset for basic teleoperation.A modular sensing bay can accommodate the DHT11 temperaturehumidity sensor, the BMP280 barometric sensor, the HC-SR04 ultrasonic ranger, soil-moisture and light probes, MQ-series gas detectors, and the MPU6050 inertial unit.The chassis is designed in CAD for fabrication from acrylic sheet, keeping the projected build cost within a few thousand Indian rupees.The review synthesises peer-reviewed literature indexed in major scholarly databases, supplemented by an authenticated NASA technical memorandum, spanning flight autonomy on Perseverance, deep-learning terrain classification, rocker-bogie suspension optimisation, lowcost sensor calibration, and meta-analytic findings on robotics-supported learning.Five recurring management concerns emerge: purpose, hardware, sensing, process, and reliability.We organise these into a development framework for student-built rovers and map a staged upgrade path toward future TinyML inference, autonomous obstacle avoidance, GPS-assisted navigation, and IoT telemetry.AI is therefore positioned as a future capability rather than a demonstrated onboard function, with the present design providing the sensing, control, and data-acquisition foundation for subsequent TinyML deployment.The platform is an educational terrestrial rover, not flightqualified planetary hardware.The literature supports hands-on robotics as a means of developing computational thinking and STEM engagement, while the proposed platform provides a practical setting for such learning.

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Publication Details

Journal
International Journal of Innovative Research in Technology
Published
2026-09-16
DOI
https://doi.org/10.64643/ijirt.208523-459
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
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article

An Affordable Six-Wheel Arduino Rover for Hands-On Learning in Planetary Robotics and Environmental Sensing with A Path Toward Edge AI

Dr. Kirankumar Premchand Johare, Shivraj Padmakar Shinde
International Journal of Innovative Research in Technology
Robotic Path Planning Algorithms
article

An Affordable Six-Wheel Arduino Rover for Hands-On Learning in Planetary Robotics and Environmental Sensing with A Path Toward Edge AI

Dr. Kirankumar Premchand Johare, Shivraj Padmakar Shinde
article en

Abstract

This work reviews recent literature (2021-2026) on low-cost rover platforms, educational robotics, six-wheel mobility, environmental sensing, and embedded machine learning, and positions an affordable six-wheel Arduino rover within this landscape.The platform pairs an Arduino Uno with six DC geared motors driven through an L298N dual H-bridge, while an HC-05 Bluetooth module links the chassis to an Android handset for basic teleoperation.A modular sensing bay can accommodate the DHT11 temperaturehumidity sensor, the BMP280 barometric sensor, the HC-SR04 ultrasonic ranger, soil-moisture and light probes, MQ-series gas detectors, and the MPU6050 inertial unit.The chassis is designed in CAD for fabrication from acrylic sheet, keeping the projected build cost within a few thousand Indian rupees.The review synthesises peer-reviewed literature indexed in major scholarly databases, supplemented by an authenticated NASA technical memorandum, spanning flight autonomy on Perseverance, deep-learning terrain classification, rocker-bogie suspension optimisation, lowcost sensor calibration, and meta-analytic findings on robotics-supported learning.Five recurring management concerns emerge: purpose, hardware, sensing, process, and reliability.We organise these into a development framework for student-built rovers and map a staged upgrade path toward future TinyML inference, autonomous obstacle avoidance, GPS-assisted navigation, and IoT telemetry.AI is therefore positioned as a future capability rather than a demonstrated onboard function, with the present design providing the sensing, control, and data-acquisition foundation for subsequent TinyML deployment.The platform is an educational terrestrial rover, not flightqualified planetary hardware.The literature supports hands-on robotics as a means of developing computational thinking and STEM engagement, while the proposed platform provides a practical setting for such learning.

International Journal of Innovative Research in TechnologyVol. 13(5)
G.S. Science, Arts And Commerce College (IN)
Openalex Percentile: Top 13%
Robotic Path Planning Algorithms
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