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Model-Based Control and External Load Estimation of an Extensible Soft Robotic Arm.

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This study models soft robotic arms to estimate external loads using their compliant behavior. An improved method reduces estimation errors by compensating for pressure deadzones, showing potential for enhanced robotic sensing.

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Area of Science:

  • Robotics
  • Mechanical Engineering
  • Control Systems

Background:

  • Soft robotics offers safe, adaptive interactions due to inherent compliance.
  • Utilizing soft behavior for sensing remains an under-explored area.
  • This can reduce sensor dependency and improve control strategies.

Purpose of the Study:

  • To develop a state-change model for a soft robotic arm.
  • To demonstrate the use of compliant behavior for external load estimation.
  • To improve load estimation accuracy by addressing pressure deadzone effects.

Main Methods:

  • Developed a state-change model for soft robotic arm dynamics.
  • Implemented a load estimation procedure based on the model.
  • Proposed an enhanced procedure incorporating pressure deadzone compensation.

Main Results:

  • The developed model effectively relates compliant behavior to external loads.
  • The improved estimation procedure significantly reduced errors compared to the baseline.
  • Experimental validation confirmed the potential effectiveness of the proposed methods.

Conclusions:

  • Compliant behavior in soft robotics is a valuable source of information.
  • The state-change model and compensation techniques enhance load estimation accuracy.
  • This work highlights a promising direction for sensor reduction and advanced control in soft robotics.