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.
Fig 1: Basic model of embedded magnet and Hall effect sensor in silicone cylinder at multiple positions. The magnetic flux field lines generated by the magnet are the same but interact differently with the sensor in different orientations. On the left is a silicone cylinder with bend angle 0; the right has a silicone cylinder with bend angle φ.
Fig 2: 1. Bottom mold with with silicone poured and component placeholder arms installed 2. Placeholder arms removed after silicone sets, leaving press-fit insets for components 3. Components (magnet and Hall effect sensor) fit into place inside silicone 4. Top piece of mold added, and more silicone is poured to complete the sensor 5. Sensor is removed from mold once set, ready for use 6. Actual image of sensor plus scalebar after being fully constructed using the process described above.
Fig. 3. Complete data of 3 independent lightbulb experiments. Vertical lines cross into plots taken from the same experiment. These lines show repeatable spikes when an attempt is made to tighten the lightbulb after it has been completely screwed in the socket. All experiments were run with 1000 particles, at 60Hz. Experiment 1 (left) Demonstrates 3 attempts to further tighten the lightbulb. Experiment 2 (middle) Also demonstrates 3 attempts to tighten the bulb more than possible. Experiment 3 (right) Demonstrates a singular, much more concerted effort to tighten the bulb further than possible, which instigates a much more significant spike due the longer attempt. All three of these experiments show how this sensor is able to produce results accurate enough to interpret states in delicate tasks.