About the project
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Our research focuses on the development of advanced robotic systems capable of autonomous navigation and interaction within dynamic environments. Key areas of investigation include:
- computer vision techniques
- machine learning models for decision making
- precision control algorithms
Setup for testing and calibrating the robotic drill system.
The robotic drill system is designed to perform precise drilling operations in challenging environments. It incorporates advanced sensor systems and adaptive control mechanisms to ensure optimal performance.
Drill system setup.
The project aims to develop a bone drill that will increase safety and precision compared to conventional drilling procedures. The drill uses a two-axis setup where one axis is the rotation of the drill, and the other is the axial movement of the drill. The rotational axis uses velocity control where the optimal rpm is determined experimentally. The axial movement is controlled through axial force control, and the optimal force is also determined experimentally, the control loop is closed using an integrated force sensor.
For better control, a feedforward signal is generated from the bone density profile along the drilling trajectory that is precomputed from the CT scan of the skull. This way we can indirectly limit the bone temperature and stop bone necrosis from emerging. To prevent brain damage from the drilling procedure, a multi-layer safety stopping mechanism is implemented. From the CT scan of the skull the skull thickness at the drilling point will be determined, and that distance is used as the final safety stop. This distance will be increased by a predefined value to ensure full drill passthrough.
The second drill stoppage mechanism will be based on the derivative of the sensed force from the force sensor. This derivative is calculated in real time and allows for more precise stopping with minimal passthrough while ensuring full hole clearance.
The AI system integrates various technologies to enable real-time decision making and adaptive responses. It leverages machine learning algorithms and sensor data to interpret the environment and execute tasks autonomously.
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.