Science Robotics

Supplementary Materials

The PDF file includes:

  • Note S1. State of the art of multimodal sensing structures.
  • Note S2. Influence of temperature on the ionic liquid signal.
  • Note S3. Influence of deformation sequence on the mode classification.
  • Note S4. Simulation setup.
  • Note S5. Readout circuit.
  • Note S6. Threshold evaluation for detecting single-mode deformation.
  • Note S7. ANN architecture for detecting single-mode deformations.
  • Note S8. ANN architecture for decoupling multimode deformations.
  • Fig. S1. Microscopic images showing the shape changes of weaving patterns of the conductive fabric depending on different deformation states.
  • Fig. S2. Simulation results of all three sensing mechanisms with different deformation inputs.
  • Fig. S3. Sensor characterization setup for single-mode deformation testing.
  • Fig. S4. Cyclic test results of loading and unloading loops for 200 cycles.
  • Fig. S5. Calibration result of optical sensing signals.
  • Fig. S6. Bending and compression tests on different deformation locations.
  • Fig. S7. Results of bending and compression tests on different deformation locations.
  • Fig. S8. Experimental setup for thermal characterization of the ionic liquid sensor.
  • Fig. S9. Temperature sensitivity of the ionic liquid sensor for 1500 s with different temperature conditions.
  • Fig. S10. Classification algorithm for estimating single-mode deformations based on threshold evaluation.
  • Fig. S11. Architecture of the ANN used for estimating single-mode deformation.
  • Fig. S12. Classification result of multimode deformation applied in different sequences.
  • Fig. S13. Application examples of human-robot interfaces.
  • Fig. S14. System configurations of the wearable sensing devices.
  • Fig. S15. Wearable controller application setup.
  • Fig. S16. Demonstration of remote control of the sUAV for different motion tasks and the corresponding sensor data.
  • Fig. S17. Fabrication process.
  • Fig. S18. Sensor signal readout circuit.
  • Fig. S19. Test setups for multimode deformation sensing.
  • Table S1. Characteristic of optical signals.
  • Table S2. Comparison between the fabrication process of the proposed sensor and existing multimodal sensors.
  • Table S3. Summary of the state of the art of multimodal sensing structures.
  • Reference (94)

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Other Supplementary Material for this manuscript includes the following:

  • Movie S1 (.mp4 format). FEA simulation of three sensing mechanisms.
  • Movie S2 (.mp4 format). Characterization of single-mode deformation.
  • Movie S3 (.mp4 format). Characterization of multimode deformation.
  • Movie S4 (.mp4 format). Sensing of single-mode deformations.
  • Movie S5 (.mp4 format). Sensing of multimode deformations.
  • Movie S6 (.mp4 format). Wearable device for wrist and elbow motion sensing.
  • Movie S7 (.mp4 format). Remote manipulation of the robotic arm with a gripper.
  • Movie S8 (.mp4 format). Remote control of the uncrewed aerial vehicle.
  • Movie S9 (.mp4 format). Remote control of the commercial robotic arm and contact recognition.
  • Movie S10 (.mp4 format). Interactive multi-DoF soft robot.

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