Optimisation of Robotic Sorting for Metal Recycling

In this doctoral research, it is investigated how the sorting of metal with gripping arms can be optimized, in order to make metal sorting economically viable. The emphasis of the research will lie in the optimization of the software, where various sensors will used to help with the gripping algorithm. Some of the primary lines of thought lie in the use of machine learning algorithms, such as reinforcement learning, where the software will adapt itself based on the data from previous actions.

Keywords:Gripping arm, Reinforcement Learning, Metal recycling, Metal sorting, Random Bin Picking

Disciplines:Adaptive agents and intelligent robotics, Robot manipulation and interfaces

Project type:PhD project