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

TL;DR

This paper combines physics-informed neural networks and robotic process automation to improve robotic joint control and motion planning.

Contribution

The novel integration of PINN techniques with RPA tools enhances robotic control precision and efficiency.

Findings

PINN techniques like Extended PINNs and Hybrid PINNs reduce training costs and improve convergence.

Combining PINNs with RPA tools streamlines robot control and motion planning in dynamic environments.

PDE-Inspired PINNs integrated with ROS and RPA improve real-world robotic navigation and manipulation.

Abstract

This paper explores the integration of Physics-Informed Neural Networks (PINNs) and Robot Process Automation (RPA) tools in modeling and controlling rigid robotic joint motion. PINNs, which integrate physical laws with neural networks, offer a promising solution for solving both forward and inverse problems in robotics, while RPA tools provide the means to automate and streamline these processes. The study discusses various PINN techniques, including Extended PINNs, Hybrid PINNs, and Minimized Loss techniques, developed to address issues such as high training costs and slow convergence rates. By combining these advanced PINN approaches with RPA tools, the research aims to enhance the precision and efficiency of robot control, motion planning, and process automation, particularly in non-linear and dynamic coupling situations. We also examine PDE-Inspired PINNs for motion planning in robot navigation and manipulation by integrating it with ROS using the RPA tool itself for coordinating joints and angle movements, and exploring how RPA can facilitate the implementation of these models in real-world scenarios.

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