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This work presents an affordable educational robot designed to make robotics accessible in resource-constrained settings. Using low-cost hardware, the platform costs just $57.63 55–66% less than comparable solutions, while offering autonomous obstacle avoidance and Wi-Fi teleoperation through an ROS-based architecture. Unlike similar platforms, this solution combines ROS-based autonomy, full simulation compatibility, and real-world classroom validation. Our main contribution lies in bridging advanced robotics concepts with low-cost and real-world education. Deployed in undergraduate courses at Universidade do Estado de Santa Catarina, the robot demonstrated strong learning efficacy. Students rated achievement of objectives at 4.5/5 and affordability at 4.3/5, with a 35% improvement in post-test scores. Key competencies were reinforced (mean experience score: 3.75/5), though the activity’s difficulty was deemed appropriate (3.3/5 for “Easy lesson”). Interdisciplinary connections emerged in control systems (68%) and algorithms (72%). Challenges included Sim-to-Real calibration issues (40% of groups) and wheel slippage (25% odometry errors). The modular design and CoppeliaSim compatibility ensure adaptability for diverse curricula. By combining a sub-$60 hardware platform with proven pedagogical outcomes, this work enables scalable robotics education without sacrificing quality, supporting advanced concepts like multi-robot systems and sensor fusion. Results affirm that well-designed low-cost solutions can rival commercial platforms, democratizing global access to hands-on robotics training. Beyond classrooms, the platform was successfully adapted for training research assistants at UDESC’s LABIND, bridging ROS theory and applied electronics for RoboCup soccer robots. The hands-on assembly and modular design accelerated competency development, with open-source materials enabling reproducibility. This extension confirms the platform’s versatility for both introductory and advanced robotics education, with potential applications in multi-robot systems and embedded AI.

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Open-Source Educational Robot for Resource-Constrained Environments

  • 会议论文

  • 发表日期：

    2026年

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  • Douglas Favaretto,
  • Vitor de Assis,
  • Dieisson Martinelli,
  • Andre Schneider De Oliveira,
  • Vivian Cremer Kalempa

摘要

This work presents an affordable educational robot designed to make robotics accessible in resource-constrained settings. Using low-cost hardware, the platform costs just $57.63 55–66% less than comparable solutions, while offering autonomous obstacle avoidance and Wi-Fi teleoperation through an ROS-based architecture. Unlike similar platforms, this solution combines ROS-based autonomy, full simulation compatibility, and real-world classroom validation. Our main contribution lies in bridging advanced robotics concepts with low-cost and real-world education. Deployed in undergraduate courses at Universidade do Estado de Santa Catarina, the robot demonstrated strong learning efficacy. Students rated achievement of objectives at 4.5/5 and affordability at 4.3/5, with a 35% improvement in post-test scores. Key competencies were reinforced (mean experience score: 3.75/5), though the activity’s difficulty was deemed appropriate (3.3/5 for “Easy lesson”). Interdisciplinary connections emerged in control systems (68%) and algorithms (72%). Challenges included Sim-to-Real calibration issues (40% of groups) and wheel slippage (25% odometry errors). The modular design and CoppeliaSim compatibility ensure adaptability for diverse curricula. By combining a sub-$60 hardware platform with proven pedagogical outcomes, this work enables scalable robotics education without sacrificing quality, supporting advanced concepts like multi-robot systems and sensor fusion. Results affirm that well-designed low-cost solutions can rival commercial platforms, democratizing global access to hands-on robotics training. Beyond classrooms, the platform was successfully adapted for training research assistants at UDESC’s LABIND, bridging ROS theory and applied electronics for RoboCup soccer robots. The hands-on assembly and modular design accelerated competency development, with open-source materials enabling reproducibility. This extension confirms the platform’s versatility for both introductory and advanced robotics education, with potential applications in multi-robot systems and embedded AI.