Traditional industrial robot arms are widely used in today's manufacturing processes. They are very precise and versatile. However, due to their weight, their use cases are very limited in mobile applications, aerospace or human-machine interaction. The development of lightweight manipulators is therefore necessary in these fields of application. Not only are they easier to transport, but as a result of their lower inertia, faster movement is also possible. This comes with consequences though. The robot arm is far more flexible and vibrations are induced by every movement. Therefore, a suitable control algorithm needs to damp and prevent them if possible.
In this thesis a two joint robot arm mounted on a flexible base will be controlled using the SpiNNaker2 platform. With its 152 ARM Cortex M4 processors per chip the SpiNNaker2 provides massive parallel execution and fast neural network computation capabilities.
Reinforcement learning is used and implemented on this hardware to control the manipulator. This not only tests the suitability of Reinforcement Learning for this control problem, but also the SpiNNaker's performance in this regard. As comparison, an input shaping approach, where the joint trajectories are modified to compensate vibrations, is implemented as well.
This presentation will highlight the progress I have made so far including modelling of the robot arm, the control algorithms and their implementation, the hardware setup as well as some simulation results.