Reference Adaptation for Robots in Physical Interactions With Unknown Environments.

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Robots operating in unknown physical environments face challenges in adapting their behavior.
• Effective physical interaction requires balancing trajectory tracking with controlled interaction forces.

Purpose of the Study:

• To propose a reference adaptation method for robots in unknown environments.
• To enhance robot performance during physical interactions by minimizing a defined cost function.

Main Methods:

• A cost function integrating trajectory tracking error and interaction force was developed.
• Reference adaptation was achieved through trajectory parametrization and iterative learning.
• Adaptive impedance control was implemented to align robot behavior with a target impedance model.

Main Results:

• The proposed method effectively adapted robot references in unknown environments.
• Simulation and experimental studies validated the method's performance.
• The approach successfully minimized the combined cost function.

Conclusions:

• The developed reference adaptation technique improves robot control in uncertain physical interactions.
• This method offers a robust solution for enhancing robot safety and task execution.
• The findings contribute to advancing autonomous robot capabilities in real-world scenarios.