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When two objects come in direct contact with each other, it is called a collision. During a collision, two or more objects exert forces on each other in a relatively short amount of time. A collision can be categorized as either an elastic or inelastic collision. If two or more objects approach each other, collide and then bounce off, moving away from each other with the same relative speed at which they approached each other, the total kinetic energy of the system is said to be conserved. This...
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Collision-Free Compliance Control for Redundant Manipulators: An Optimization Case.

Xuefeng Zhou1, Zhihao Xu1, Shuai Li2

  • 1Guangdong Key Laboratory of Modern Control Technology, Guangdong Institute of Intelligence Manufacturing, Guangzhou, China.

Frontiers in Neurorobotics
|August 10, 2019
PubMed
Summary

This study introduces a novel collision-free compliance control strategy for redundant manipulators using recurrent neural networks (RNNs). The method enhances robotic safety and efficiency by managing forces and avoiding obstacles in real-time.

Keywords:
compliance controlobstacle avoidancerecurrent neural networkredundant manipulatorzeroing neural network

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Force control is crucial for manipulator compliance and execution.
  • Redundant manipulators present challenges, particularly with collision risks.
  • Existing methods may struggle with real-time collision avoidance and compliance.

Purpose of the Study:

  • To develop a collision-free compliance control strategy for redundant manipulators.
  • To integrate recurrent neural networks (RNNs) for real-time constraint optimization.
  • To enhance manipulator safety and energy efficiency during operation.

Main Methods:

  • Rebuilt position-force control as task-space velocity commands inspired by impedance control.
  • Formulated compliance as an equality constraint at the joint velocity level using kinematic properties.
  • Developed a collision avoidance strategy using key points to define a feasible workspace.
  • Incorporated a secondary task to minimize joint velocities for energy saving and impact reduction.
  • Established a provably convergent RNN to solve the real-time constraint-optimization problem.

Main Results:

  • The proposed strategy effectively manages force control and enhances manipulator compliance.
  • Real-time collision avoidance was achieved by dynamically scaling the feasible workspace.
  • Minimizing joint velocities contributed to reduced energy consumption and potential collision impact.
  • Numerical simulations validated the controller's effectiveness and real-time performance.

Conclusions:

  • The recurrent neural network-based collision-free compliance control strategy is effective for redundant manipulators.
  • The approach successfully addresses challenges in force control, compliance, and collision avoidance simultaneously.
  • This method offers a promising solution for safer and more efficient robotic operations in complex environments.