Adaptive heading correction for an industrial heavy-duty omnidirectional robot

Area of Science:

• Robotics and Automation
• Industrial Engineering
• Control Systems

Background:

• Mecanum wheels (MW) offer omnidirectionality for industrial robots, enabling maneuverability in confined spaces.
• However, MWs present challenges like rolling radius variability, leading to slippage, vibrations, and reduced accuracy in pose estimation and control.
• These issues hinder the widespread adoption of MW robots for heavy-duty and long-duration industrial applications.

Purpose of the Study:

• To design and test an adaptive estimation framework for industrial robots utilizing mecanum wheels.
• To mitigate dynamic ill-effects such as slippage and vibrations caused by variations in the mecanum wheel rolling radius.
• To enhance the accuracy of pose estimation and tracking control systems in real-world industrial environments.

Main Methods:

• Developed a robot prototype with mecanum wheels for industrial applications.
• Modeled the kinematic and dynamic behavior of the robot system.
• Implemented an adaptive estimation framework to adjust the robot control system based on traversed surface properties.

Main Results:

• The adaptive framework effectively addressed dynamic ill-effects like slippage and vibrations.
• Improved accuracy in pose estimation and tracking control was achieved.
• Experimental validation demonstrated the system's efficacy in real industrial settings.

Conclusions:

• The proposed adaptive estimation framework successfully enhances the performance and reliability of mecanum wheel robots in industrial settings.
• This approach overcomes key limitations, paving the way for broader deployment of omnidirectional robots in demanding applications.
• The study validates the effectiveness of adaptive control strategies for compensating for surface-dependent dynamics in robotic systems.