Industrial mobile robots face critical positioning challenges that impact manufacturing efficiency, warehouse automation productivity, and biomedical service delivery. This paper presents a reproducible framework for quantifying odometric drift in differential-drive robots, validated by consumer-grade, low-cost VR tracking. Applications include industrial automation calibration, warehouse logistics management, and precision biomedical device positioning. Through more than 750 automated experimental trials spanning a comprehensive matrix of motor configurations and path geometries, the results show that both path complexity and turn size significantly influence drift patterns. Specifically, routes with higher geometric complexity (12-15 segments) exhibited 22% greater position error than simpler paths. The analysis used advanced metrics such as the Normalized Drift Contribution Index. The results confirm robust, high-resolution drift analysis and provide a low-cost validation tool for robot calibration in manufacturing and medical instrumentation. The work provides actionable insights for optimizing robot programming, calibration, and curriculum design, and establishes a scalable protocol for benchmarking autonomous navigation systems in real-world scenarios. In addition, the methodology enables data-driven decision making for robot fleet management, reducing operational downtime compared to manual calibration methods, while providing quantitative performance benchmarks essential for industrial quality control standards.

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