Fast Probabilistic 3-D Curvature Proprioception with a Magnetic Soft Sensor


This paper introduces a cost-effective and high speed approach for predicting a 2-DOF bend parameterization for soft bodies through a magnetic and constant curvature system. We propose a design for a probabilistic particle filter that can be paired with magnetic simulations to produce highly accurate and fast pose information for parameter-constrained magnets. We include the design, fabrication, modeling, and experimental results of a physical sensor with the ability to produce both bend directionality and bend magnitude results with a speed of ~60Hz. The proposed design consists of a magnet and tri-axis Hall effect sensor embedded in a soft silicone body. We demonstrate the effectiveness of this system through real-world interaction tests.

In IEEE 17th International Conference on Automation Science and Engineering