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Biologically Inspired Robotics Laboratory

Department of Engineering

Strain Vector Aided Sensorization of Soft Structures (SVAS│)

Soft material structures exhibit high deformability and conformability which can be useful for many engineering applications such as robots adapting to unstructured and dynamic environments. However, the fact that they have almost infinite degrees of freedom challenges conventional sensory systems and sensorization approaches due to the difficulties in adapting to soft structure deformations. We are addressing this challenge by proposing a novel method which designs flexible sensor morphologies to sense soft material deformations by using a functional material called conductive thermoplastic elastomer (CTPE).

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Figure 1. A conceptual schematics of the SVAS│ approach. Three examples of soft bodies are deformed (shown with red arrows) and sensorized with CTPE-based sensors (shown with black curves).

This model-based design method, called Strain Vector Aided Sensorization of Soft Structures (SVAS│), provides a simulation platform which analyzes soft body deformations and automatically finds suitable locations for CTPE-based strain gauge sensors to gather strain information which best characterizes the deformation. Our chosen sensor material CTPE exhibits a set of unique behaviors in terms of strain length electrical conductivity, elasticity, and shape adaptability, allowing us to flexibly design sensor morphology that can best capture strain distributions in a given soft structure.

Conductive Thermoplastic Elastomer for Strain Sensing

In our approach we use a conductive thermoplastic elastomer (CTPE) developed by EMPA for giant strain sensing, e.g., above 100% reversible strain length. The material is based on a commercial thermoplastic elastomer matrix filled with 50 wt% carbon black powder which makes this hybrid a candidate for a piezoresistant sensor material. This composition is mixed in a high shear mixer to blend the polymer with the inorganic conductive powder at a temperature of 180 ░C. The extracted compound has conductive, thermoplastic and elastic properties which are exploited during fabrication of the sensors, as well as in the sensing mechanism itself.

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Figure 2. Thermoplastic and mechanical properties of CTPE. (a) Fabrication process that can easily generate arbitrary forms. (b) Mechanical and (c) electrical properties of CTPE when shaped into fibers adapted from previous work by EMPA.

SVAS│ Design Method

In continuum mechanics, when a force is applied to a solid material it undergoes a deformation, whose mechanical properties can be analyzed with the relationship between the stress in the body that the force causes and the strain that occurs during the deformation of the body. In classical terms, this relation can be expressed with Hooke’s Law:


\x{03c3} = F / A = E \x{03b5}


where the stress \x{03c3} is generated by the force F on a cross section of A on the material. The resulting strain \x{03b5} is dependent on the elastic properties of the material, and can be dictated by its Young’s modulus E, as long as the material shows elastic and reversible deformation while the applied stress is below the yield stress.

Deformations in soft bodies can also be explained by the same formula as long as the structure does not exhibit plastic deformations. Following this idea, we hypothesize that for every complex deformation, there exists a unique and representative strain information. In our approach, we use this strain information and its geometric properties to design morphologies for flexible sensors which are responsive to strain. Sensor morphologies are designed by following five consecutive steps: (1) soft body and elastic deformation modeling in VoxCAD; (2) strain vector extraction; (3) strain region clustering and (4) path planning for final morphology formation. These four steps end up with a final strain path where fiber shaped sensors, which are fabricated with CTPE material, can be placed on to gather strain information and estimate the sensor response to the selected deformations.

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Figure 3. Overall process of SVAS│ explained with an example soft structure block. (a) Soft body constructed with voxels, (b) constraints and stimulus applied and (c) soft body deformation simulated. (d) Strain vectors are extracted from deformations and (e) clustered to generate the final sensor morphology.

Sensor Morphologies for Soft Robotic Behaviors

In soft robotics research, there is a tendency for robots’ motion patterns to imitate their biological counterpart in some ways. Two of the most observed and imitated behaviors are the serpentine and twisting motions. While the former can be modeled with a translational deflection of the body on a single axis, the latter one can be explained with a torsional twist around a chosen axis. Therefore, in this example, we show the SVAS│ method applied to both of these motions in order to get the sensor morphology that is best to discriminate them from each other.

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Figure 4. An overview of the approach which generates sensor morphologies to discriminate (a) serpentine and (b) twisting motions on soft elastic silicone blocks. First row shows images of deformed silicone blocks in VoxCad simulations where lighter colors represent larger strains. Second row represents extracted strain vectors from the surface of deformed bodies. Last row shows the final sensor morphologies with straight black lines on top the clustered strain regions which are shown with different colors.

Resources

This research has been published in Sensors Journal [1] and IROS 2014 Conference [2], and details can be found from these publications. Additionally, the source code of the whole approach can be found from this link.

Publications

  1. Culha, U., Nurzaman, S.G., Clemens, F., and Iida, F. (2014), SVAS3: Strain vector aided sensorization of soft structures, Sensors, 14(7):12748-12770.
  2. Culha, U., Wani, U., Nurzaman, S.G., Clemens, F., and Iida, F., Motion pattern discrimination for soft robots with morphologically flexible sensors. The 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), 14-18 September 2014, Chicago, USA. 567-572.