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Robust Multimodal Indirect Sensing for Soft Robots Via Neural Network-Aided Filter-Based Estimation.

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This study introduces a novel recurrent neural network-based adaptive unscented Kalman filter (RNN-AUKF) for soft robot sensing. The method enables robust multimodal indirect sensing using minimal sensors, overcoming integration challenges.

Keywords:
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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Sensor Technology

Background:

  • Integrating sensors into soft robots is difficult due to their flexibility.
  • Indirect sensing using estimation schemes is an alternative but requires complex modeling and robustness.
  • Existing methods struggle with multimodal sensing and sensor fault tolerance.

Purpose of the Study:

  • To develop a robust indirect sensing scheme for soft robots.
  • To enable multimodal sensing (internal and external variables) with minimal sensors.
  • To ensure the estimation scheme is resilient to sensor degradation and faults.

Main Methods:

  • Proposed a recurrent neural network-based adaptive unscented Kalman filter (RNN-AUKF) architecture.
  • Utilized a data-driven approach with Recurrent Neural Networks (RNNs) to model complex soft robot dynamics.
  • Integrated an unknown input estimator with the RNN-AUKF for multimodal sensing via a single flex sensor.
  • Mathematically proved bounded estimation error against sensor noise and drift.

Main Results:

  • The RNN-AUKF demonstrated superior accuracy and robustness compared to benchmark methods.
  • The scheme was successfully extended to a multifinger soft gripper.
  • The proposed method showed resilience to out-of-distribution sensor dynamics.

Conclusions:

  • The RNN-AUKF offers a viable solution for robust multimodal indirect sensing in soft robots.
  • This approach significantly enhances soft robot perception capabilities.
  • The findings have broad implications for the future development of advanced soft robotic systems.