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Learning-based control for tendon-driven continuum robotic arms.

Nima Maghooli1, Omid Mahdizadeh1, Mohammad Bajelani1

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This study introduces a novel Deep Reinforcement Learning control strategy for tendon-driven continuum robots. The model-free controller enhances trajectory tracking and adapts to real-world conditions, outperforming traditional methods.

Keywords:
data-driven controldeep deterministic policy gradient algorithmdeep reinforcement learninglearning-based controlmodified transpose Jacobianoptimal adaptive gain-tuning systemsim-to-real transfertendon-driven continuum robots

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Tendon-driven continuum robots offer flexibility but face challenges due to nonlinear dynamics.
  • Classical model-based controllers struggle with inherent uncertainties in these systems.
  • Advanced, adaptive control strategies are needed for diverse operational scenarios.

Purpose of the Study:

  • To develop a model-free, centralized position control strategy for tendon-driven continuum robots.
  • To enhance Sim-to-Real transfer of control policies for these robots.
  • To achieve superior trajectory-tracking performance compared to model-based strategies.

Main Methods:

  • Utilized Deep Reinforcement Learning (DRL) with a customized Modified Transpose Jacobian control strategy.
  • Optimally tuned control parameters using the Deep Deterministic Policy Gradient (DDPG) algorithm.
  • Integrated optimal adaptive gain-tuning regulation for a model-free approach.

Main Results:

  • The proposed DRL-based controller significantly improved trajectory-tracking performance in simulations and real-world experiments.
  • Demonstrated robustness across various initial conditions and complex trajectories.
  • Achieved superior performance compared to ideal model-based control strategies.

Conclusions:

  • The developed model-free controller offers a promising solution for controlling continuum robots.
  • The strategy enhances adaptability and performance in constrained environments.
  • The approach is suitable for general-purpose applications requiring precise robotic control.