Robotic Welding Optimization using A* Parallel Path Planning and Advanced Machine Learning - Tiago Martins Couto (2021)
The world of robotics is in constant evolution, trying to find new solutions to improve on top of the current technology and to overcome the current industrial pitfalls. To date, one of the key intelligent robotics components, path planning algorithms, lack flexibility when considering dynamic constraints on the surrounding work cell. This is mainly related to a large amount of time required to generate safe collision-free paths for high redundancy systems. Furthermore, and despite the already known benefits, the adoption of CPU/GPU parallel solutions is still lacking in the robotic field. On top of this, welding physics is complex, and therefore the welding parametrization is time-consuming. In manual welding, the "hand", the experience, and the best sensor of all (the eyes) can compensate for the difficulties in finding the right settings (welding parameters, robot posture, speed, ...) for a specific weld seam. In robotic welding, the robotic arm and the sensors are limited, and the parametrization time escalates. The main goal of this project is to optimize robot welding, by developing a flexible welding robotized system, through the introduction of (knowledge-based) decision support for welding parametrization in an advanced robotic work cell, in combination with advanced (collision-free) offline programming and advanced sensing, and improve path planning by developing a software platform capable of interconnecting the path planning algorithms with parallel computing tools, reducing the time needed to generate a safe path. This project will also investigate the current state of robotics and existing solutions for path planning problems, as well as machine learning algorithms and the most important parameters for welding.