ReviewPeerJ. Computer science2025
The line follower robot: a meta-analytic approach.
Review in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Line-follower robots represent a critical segment in autonomous robotics, with broad applications ranging from industrial automation to educational tools. This meta-analytic review synthesizes research on line-follower robots, addressing a noticeable gap in the literature where comprehensive analyses are scarce. The review leverages the Theory of the Consolidated Meta-analytic Approach (TEMAC) to systematically explore 287 documents spanning from 2001 to 2024, highlighting key contributions, trends, and gaps in the field. Through this analysis, it becomes evident that while significant advancements have been made in control strategies, sensor integration, and noise reduction techniques, the literature still lacks comprehensive studies on the scalability of these technologies, especially in large-scale industrial environments. Recent research trends emphasize integrating artificial intelligence and machine learning into line-follower robots, indicating a shift towards more sophisticated, adaptable systems. Despite these advancements, challenges remain in addressing environmental variability, improving real-time adaptability, and exploring novel applications in dynamic environments. This review not only maps the historical evolution and current state of line-follower robots but also identifies future research directions that could drive the next generation of robotic systems. The findings offer valuable insights for researchers, engineers, and educators aiming to enhance the efficiency, reliability, and application scope of line-follower robots.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.