Automating PINN-based kinematic resolution of robotic joints using robotic process automation frameworks

Area of Science:

• Robotics
• Artificial Intelligence
• Computational Science

Background:

• Physics-Informed Neural Networks (PINNs) offer a novel approach to solving complex problems in robotics by integrating physical laws into neural network training.
• Robot Process Automation (RPA) tools can streamline and automate robotic tasks, but their integration with advanced modeling techniques requires further exploration.
• Existing challenges in robotic motion control include high training costs and slow convergence rates for traditional methods.

Purpose of the Study:

• To explore the synergistic integration of PINNs and RPA tools for modeling and controlling rigid robotic joint motion.
• To investigate advanced PINN techniques (Extended PINNs, Hybrid PINNs, Minimized Loss) for overcoming training inefficiencies.
• To demonstrate the application of PDE-Inspired PINNs with RPA for robot navigation, manipulation, and real-world process automation.

Main Methods:

• Implementation of various PINN architectures, including Extended PINNs, Hybrid PINNs, and Minimized Loss techniques.
• Integration of PINN models with RPA tools for automating robotic control and motion planning processes.
• Utilizing the Robot Operating System (ROS) in conjunction with RPA for coordinating joint movements and angle control in robotic systems.

Main Results:

• The study demonstrates that combining advanced PINNs with RPA tools can significantly enhance the precision and efficiency of robot control.
• Advanced PINN techniques effectively address issues of high training costs and slow convergence rates.
• PDE-Inspired PINNs, when integrated with RPA and ROS, show promise for sophisticated motion planning in navigation and manipulation tasks.

Conclusions:

• The integration of PINNs and RPA presents a powerful framework for advanced robotic modeling and control.
• This hybrid approach facilitates more efficient and precise automation of complex robotic operations, especially in non-linear and dynamic scenarios.
• The research paves the way for practical, real-world applications of sophisticated AI-driven robotic systems.