Hybrid Robotics for Future Reconfigurable Manufacturing

Current robotic systems in manufacturing are predominantly based on the "Part-to-Process" notion, where the part is delivered and precisely positioned within a fixed manipulator robotic cell. Most such systems lack flexibility, execute a strictly choreographed task, and are unsuitable for reconfigurable manufacturing. E.g., welding and spray painting applications in the automotive sector.

The recent developments in the automation industry led to the emergence of collaborative mobile manipulator robots, particularly combining the capabilities of robotic arms with mobile platforms. These platforms offer a unique opportunity to realise the idea of "Process-to-Part" in the manufacturing industry. For example, the process can now be brought to the part at the point of manufacture, enabling future reconfigurable manufacturing systems.

This research aims to develop fundamental technology to enable mobile robotic manipulators' real-time synchronised operation and autonomous reasoning capabilities to realise the full potential in reconfigurable settings. Here, it is sought to use Deep Reinforcement Learning techniques to solve the inverse trajectory planning and reasoning problem combined with advanced sensor technologies such as 3D vision, end effector force-torque control, LiDAR, etc., to generate robotic programs with minimal human input.

E.g., once the production line is reconfigured, the robot will do autonomous reasoning to understand the new set of tasks to be executed and establish how and when to perform them without human intervention.

However, successful training of a reinforcement learning algorithm in the real world may require years of training data, attempts, and researcher time and poses a significant risk of accident and damage to the robotic hardware and its environment. Therefore, this research suggests using the "Sim-to-Real" transfer learning scheme to train a given reinforcement learning algorithm.

In Sim-to-Real, the algorithm's training phase is performed in a virtual environment by modelling the robot and its operating domain within a physics simulator. The trained algorithm will then be transferred to the real-world robot using domain adaptation or a similar technique and tested for real-time operation. This method reduces the time, effort, costs and risks of training the robot in the real world.

However, the Sim-to-Real technique may have challenges associated with physics modelling in the simulation environment and accurate knowledge transfer between multiple domains