A Novel Recurrent Neural Network for Improving Redundant Manipulator Motion Planning Completeness

Area of Science:

• Robotics
• Artificial Intelligence
• Control Systems

Background:

• Recurrent Neural Networks (RNNs) offer advantages in precision, robustness, and efficiency for redundant manipulator control.
• Current RNN schemes optimize trajectories locally, excelling in obstacle avoidance but lacking planning completeness and suffering from local minima.

Purpose of the Study:

• To address the planning incompleteness and local minimum issues in existing Recurrent Neural Network (RNN) control schemes for redundant manipulators.
• To propose and theoretically analyze a novel RNN control scheme that guarantees global stability and planning completeness.

Main Methods:

• A novel Recurrent Neural Network (RNN) control scheme was developed to address motion planning challenges.
• Theoretical analysis was conducted to establish global stability and planning completeness of the proposed method.
• Software simulations were performed to compare the novel scheme against three existing control methods.

Main Results:

• The proposed RNN control scheme demonstrated improved precision and robustness compared to existing methods.
• The novel method successfully achieved planning completeness, overcoming a key limitation of prior approaches.
• Simulations validated the theoretical findings on stability and performance.

Conclusions:

• The novel RNN control scheme effectively resolves the local minimum and planning incompleteness problems in redundant manipulator motion planning.
• This advancement offers a more reliable and complete solution for complex robotic control tasks.
• The method shows significant potential for enhancing the performance of robotic systems in dynamic environments.