Academic Fraud Detection Report: Optimal Robot Formations: Balancing Range-Based Observability and User-Defined Configurations

Academic fraud report · Geng Detector

Summary

Verdict: CLEAR (no academic fraud detected). This report examines an IROS 2024 paper (DOI: 10.1109/IROS58592.2024.10801342) on optimal robot formations balancing range-based observability with user-defined configurations. Key findings: (1) Image reuse analysis was limited to figure caption review because no raw pixel data was available; figure logic describing trajectory heatmaps and RMSE curves under different conditions showed no suspicious multi-use patterns. (2) Data authenticity in TABLE I and TABLE II was verified via last-digit distribution and cross-column variance checks, revealing natural noise patterns (e.g., 35.4%, 58.8%, 29.6%) and no evidence of constant-difference fabrication. (3) Statistical methodology is sound: 100-trial Monte Carlo runs are standard; a 3 real + 2 virtual UAV hardware-in-the-loop setup is an honest disclosure given space constraints. (4) Timeline and equipment references are consistent (2024 references, real devices like Uvify IFO-S, Vicon, DWM1000). Limitations: image-level pixel forensics could not be performed on this text-only submission, and apparent OCR artifacts should not be attributed to the authors.

Verdict

CLEAR — No evidence of academic fraud was identified. The paper presents coherent engineering methodology with internally consistent numerical reporting.

Key findings

Image integrity: Unable to perform pixel-level analysis due to text-only submission; figure captions describe distinct experimental conditions (different variables such as $x_{adj}$, $x_{opt}$, $x_{cov}$), with no suspicious cross-experimental reuse.
Numerical authenticity (TABLE I & II): Last-digit distribution of reported percentages (4, 8, 6, 6, 0, 0, 2, 4 in TABLE I; 1, 1, 2, 9, 4, 9, 4, 1 in TABLE II) shows natural variance with no clustering on 0/5 that would suggest fabricated random numbers.
Cross-column consistency: Differences between $x_{opt}$ and $x_{cov}$ columns fluctuate realistically rather than forming constant offsets, consistent with genuine measurement noise.
Experimental design: 100-iteration Monte Carlo simulations match domain norms; the disclosed 3 real UAVs + 2 virtual UAVs setup for the hardware experiments demonstrates transparency about equipment limitations.
Parameter realism: Reported filter parameters (0.1 rad/s, 0.1 m/s process noise; $0.1^2 m^2$ UWB covariance) align with commercial DWM1000 module specifications—no suspicious p-hacking toward unrealistically low noise.
Timeline and equipment plausibility: Latest citations reach 2023–2024 (e.g., arXiv:2402.08566), and cited hardware (Uvify IFO-S, Vicon motion capture, DWM1000 UWB) are genuine commercial products used in robotics research.

Evidence highlights

DOI: 10.1109/IROS58592.2024.10801342
TABLE I percentages: 35.4%, 58.8%, 29.6%, 31.6%, 47.0%, 40.0%, 66.2%, 59.4%
TABLE II percentages: 74.1%, 71.1%, 24.2%, 26.9%, 32.4%, 32.9%, 64.4%, 62.1%
Reported median errors: 0.448 m, 0.088 m, 0.032 rad — consistent with corresponding figure descriptions.
Monte Carlo trials: 100 (standard for the field)
Hardware configuration: 3 physical UAVs + 2 virtual UAVs (transparent disclosure)

Notes

The source text contains apparent OCR artifacts (e.g., "De ned", " eld", and a nonsensical page reference "p. 1 729 881 419 847 897" in reference [27], along with missing author initials in references [1], [2], [4]). These are characteristic of PDF-to-text conversion errors and should not be attributed to the authors. The authors should verify that the original PDF renders correctly.
Pixel-level image forensics could not be conducted because no embedded figure data was provided; this is a methodological limitation of text-only review.
All findings are based on logical and statistical consistency checks of the textual content. A definitive determination of academic integrity requires formal institutional investigation.