# PlatROB: An open-source, modular, and low-cost hardware platform for mobile robotics and AI education

## TL;DR

PlatROB is a low-cost, open-source robotics platform designed to teach robotics and AI through hands-on learning and system integration.

## Contribution

The novel contribution is a modular, affordable, and open-source educational robotics platform with validated performance and real-world deployment.

## Key findings

- PlatROB modules achieved validated performance metrics such as 10 kg payload support and 120 min autonomy.
- The platform was successfully used in educational settings with over 160 learners showing significant learning gains.
- All design files and code are open-source to enable replication in resource-limited environments.

## Abstract

This paper introduces PlatROB, an open-source, modular, and low-cost educational robotics platform designed to facilitate hands-on learning in robotics and AI through system integration. PlatROB comprises four 3D-printable, classroom-ready modules: an Ackermann Drive Module (ADM), an Omnidirectional/Differential Drive Module (ODM/DDM), a Control and Processing Module (CPM) with NVIDIA Jetson Nano, and a 4-DoF Articulated Manipulation Module (AMM). Inter-module communication uses standardized I2C over DB9 connectors, integrating Arduino microcontrollers, motor drivers, encoders, IMUs, and ultrasonic sensors. Module costs range from $129 to $341. Performance validation shows the ADM supports 10 kg payload, achieves 25 cm turning radius and has 120 min autonomy (3 kg load), while the CPM sustains 40–100 min operation depending on neural-network workloads. The AMM provides 450 g payload capacity for introductory manipulation tasks. Successfully deployed in workshops and university courses with over 160 learners, demonstrating significant learning gains (p < 0.05) across academic levels and enabling teleoperation and autonomous navigation projects. All mechanical and electronic design files, build manuals, and validation code are open-source to support replication in resource-constrained settings. By providing a scalable, documented hardware-software stack compatible with ROS/ROS2, PlatROB lowers barriers to experiential learning in SLAM, perception, and advanced autonomy.