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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Obstacle avoidance is crucial for robot manipulator control, particularly for systems with redundant degrees of freedom and complex physical constraints.
  • Existing methods face challenges in efficiently handling these complexities.

Purpose of the Study:

  • To propose a novel controller for robot manipulators capable of effective obstacle avoidance.
  • To address challenges posed by redundant degrees of freedom and complex physical constraints.

Main Methods:

  • A novel controller based on deep recurrent neural networks (DRNNs) was developed.
  • Robots and obstacles were abstracted into critical point sets for simplified distance description.
  • An obstacle avoidance strategy was formulated using inequality constraints and general class-K functions.
  • The control problem was framed as a quadratic programming (QP) problem under multiple constraints using the minimal-velocity-norm (MVN) scheme.
  • A DRNN considering system models was established to solve the QP problem online.

Main Results:

  • The proposed controller successfully demonstrated obstacle avoidance for both static and dynamic obstacles.
  • The controller maintained trajectory tracking capabilities while adhering to physical constraints.
  • Theoretical analysis and numerical simulations validated the controller's performance.

Conclusions:

  • The novel DRNN-based controller offers a robust solution for obstacle avoidance in redundant robot manipulators.
  • The approach effectively manages complex physical constraints and dynamic environments.
  • This method enhances the safety and efficiency of robot manipulator operations.