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RAMM: A Robotic, Autonomous Magnetic field Mapper
Shinjer Li1, Samuel J Rubin1,2, Tyler Meldrum1
1William & Mary, Department of Chemistry, 540 Landrum Drive, Williamsburg, VA 23188, USA.
Hardwarex
|October 2, 2025
Summary
We developed RAMM, a Robotic, Autonomous Magnetic field Mapper, for precise 3D magnetic field mapping. This affordable, Python-controlled system uses a Hall sensor for accurate measurements in scientific and industrial applications.
Area of Science:
- Physics
- Robotics
- Instrumentation
Background:
- Accurate spatial mapping of magnetic fields is essential for diverse scientific, industrial, and medical applications.
- Existing methods may be costly or lack comprehensive 3D measurement capabilities.
Purpose of the Study:
- To introduce RAMM (Robotic, Autonomous Magnetic field Mapper), a novel, low-cost system for precise 3D magnetic field mapping.
- To provide a flexible and accessible tool for researchers and educators.
Main Methods:
- Utilized a delta-style 3D robot equipped with a three-axis Hall sensor.
- Implemented programmatic control via a Python interface for automated volumetric measurements.
- Tested the system with various permanent magnet configurations.
Main Results:
- Demonstrated accurate measurement of magnetic field components (x, y, z) across millitesla to single-digit Tesla ranges.
- Generated detailed 3D magnetic field maps for different magnet setups.
- Validated system performance by showing agreement with manufacturer-reported field gradients.
Conclusions:
- RAMM offers an easy-to-build, affordable solution for accurate magnetic field mapping.
- The system is well-suited for both research and educational purposes.
- Facilitates volumetric magnetic field characterization with high precision.
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