Disturbance Recognition and Collision Detection of Manipulator Based on Momentum Observer

Area of Science:

• Robotics
• Control Systems
• Mechanical Engineering

Background:

• Growing demand for safe human-robot interaction necessitates advanced collision detection.
• Existing collision detection methods struggle with nonlinear disturbances like friction.
• Torque sensors are often costly and complex, driving research into sensorless methods.

Discussion:

• This research proposes a strategy to model friction as a disturbance in manipulator motion for improved collision detection.
• A manipulator joint disturbance model is established using the LuGre dynamic friction model.
• An external torque observer based on generalized momentum is designed for friction measurement.

Key Insights:

• Friction is effectively measured and modeled as a disturbance term.
• Model parameters are identified using a genetic algorithm for robustness.
• Compensating for friction-induced disturbances significantly reduces collision detection errors.

Outlook:

• The proposed method enhances collision detection accuracy and applicability across variable working conditions.
• Experimental validation confirms the accuracy of the disturbance model and the effectiveness of the collision detection method.
• This research paves the way for more reliable and safer robotic systems in diverse environments.