Global Navigation Satellite System (GNSS) Low-Cost Robotics Platform for Competition Game

In this research, the Global Navigation Satellite System (GNSS) low-cost robotics platform for competition game was designed, including the development of competition rules and field. The competition rules are specifically tailored to GNSS low-cost robot, highlighting the essential components required to build robots based on GNSS low-cost robotic platform. This research aims to promote learning and skill development in robots and GNSS, with a focus on affordable resources to ensure accessibility for students and independent developers. In research methodology, a total of 58 teams with 116 participants took part in the GNSS low-cost robotics platform competition game, including 47 high school level teams (94 participants), 6 vocational level teams (12 participants), and five undergraduate level teams ($\mathbf{1 0}$ participants). As the results, the competition game was successfully developed by the GNSS low-cost robotic platform, with over 70% of participants demonstrating a solid understanding of GNSS navigation, robot, and game design. Participants rated innovation-focused activities highly (4.55), highlighting the competition game role in inspiring creativity in GNSS and low-cost robot. In term of rule for competition game, participants gave the rates on fairness (4.23) and consistency (4.28) which were well received, although rule clarity received the lowest score (3.84), suggesting the need for clearer guidelines. In addition, the pre- and posttest assessments on participants’ knowledge, understanding, and inspiration in GNSS navigation, robot, and game design, further confirmed the effectiveness of GNSS low-cost robotics platform competition game by giving the improved mean score from 23.06 to 27.01.

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Publication Details

Published
2025-07-30
DOI
https://doi.org/10.1109/istem-ed65612.2025.11129404
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
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article

Global Navigation Satellite System (GNSS) Low-Cost Robotics Platform for Competition Game

Prasert Kenpankho, Patiphan Sumniang, Burin Putthong, Thapanan Namniwong
Robotic Path Planning Algorithms
article

Global Navigation Satellite System (GNSS) Low-Cost Robotics Platform for Competition Game

Prasert Kenpankho, Patiphan Sumniang, Burin Putthong, Thapanan Namniwong
article en

Abstract

In this research, the Global Navigation Satellite System (GNSS) low-cost robotics platform for competition game was designed, including the development of competition rules and field. The competition rules are specifically tailored to GNSS low-cost robot, highlighting the essential components required to build robots based on GNSS low-cost robotic platform. This research aims to promote learning and skill development in robots and GNSS, with a focus on affordable resources to ensure accessibility for students and independent developers. In research methodology, a total of 58 teams with 116 participants took part in the GNSS low-cost robotics platform competition game, including 47 high school level teams (94 participants), 6 vocational level teams (12 participants), and five undergraduate level teams ($\mathbf{1 0}$ participants). As the results, the competition game was successfully developed by the GNSS low-cost robotic platform, with over 70% of participants demonstrating a solid understanding of GNSS navigation, robot, and game design. Participants rated innovation-focused activities highly (4.55), highlighting the competition game role in inspiring creativity in GNSS and low-cost robot. In term of rule for competition game, participants gave the rates on fairness (4.23) and consistency (4.28) which were well received, although rule clarity received the lowest score (3.84), suggesting the need for clearer guidelines. In addition, the pre- and posttest assessments on participants’ knowledge, understanding, and inspiration in GNSS navigation, robot, and game design, further confirmed the effectiveness of GNSS low-cost robotics platform competition game by giving the improved mean score from 23.06 to 27.01.

King Mongkut's Institute of Technology Ladkrabang (TH)
Affordable and clean energy
Openalex Percentile: Top 79%
Robotic Path Planning Algorithms
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