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Adaptive Control and Optimal Trajectory Generation for Highly Dynamic Tasks on a Soft Robot.

Haley P Sanders1, Curtis C Johnson1, Marc D Killpack1

  • 1Department of Mechanical Engineering, Robotics and Dynamics Lab, Brigham Young University, Provo, Utah, USA.

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This study introduces a novel controller for soft robots, enabling them to perform dynamic tasks like throwing. The model reference adaptive controller (MRAC) allows precise trajectory tracking, improving robotic capabilities.

Keywords:
controloptimizationsoft robottrajectory planning

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Area of Science:

  • Robotics
  • Control Systems
  • Soft Robotics

Background:

  • Soft robots, pneumatically actuated, excel at dynamic tasks due to inherent energy storage and underdamped joints.
  • Controlling soft robots for high-speed movements is challenging due to complex dynamics and shape uncertainties.
  • Existing control methods struggle to achieve precise trajectory tracking required for dynamic tasks in soft robotic systems.

Purpose of the Study:

  • To develop a robust control strategy for pneumatically actuated soft robots capable of executing highly dynamic tasks.
  • To enable a three-link soft robot arm to mimic a second-order critically damped system for improved motion control.
  • To create a trajectory generation method for dynamic tasks, specifically demonstrated through a ball-throwing application.

Main Methods:

  • Formulation of a model reference adaptive controller (MRAC) to govern the soft robot arm's dynamics.
  • Implementation of a trajectory generation technique based on second-order system dynamics for precise task execution.
  • Validation of the controller and trajectory generator through both simulation and physical hardware experiments.

Main Results:

  • The MRAC controller successfully made the soft robot arm behave like a second-order critically damped system.
  • A maximum root mean square error of 0.0872 radians was reported between reference and executed trajectories in simulations and on hardware.
  • The combined controller and trajectory generator achieved an average ball-throwing accuracy within 25-28% of the target for distances of 1.5-2 meters on hardware.

Conclusions:

  • The developed MRAC provides effective trajectory tracking for dynamic tasks in soft robots, overcoming challenges posed by system uncertainties.
  • The integrated approach of MRAC and trajectory generation significantly enhances the practical capabilities of soft robots for tasks like projectile launching.
  • This research demonstrates a viable pathway for advancing the performance and applicability of large-scale soft robotic systems in complex, dynamic environments.