University of Pittsburgh engineers have developed an advanced control system for magnetic microrobots using machine learning. This technology aims to significantly improve the precision and effectiveness of microrobots for medical applications by employing artificial neural networks trained with reinforcement learning to autonomously control the movement of these tiny devices within the body.
Description
The innovative control system offers a major advancement in microrobot technology by focusing on robust, model-free navigation. This is achieved by using a camera to capture the microrobot's position in a fluidic channel, processing this data with an artificial neural network, and adjusting the magnetic fields accordingly. The optimized control system can adapt to complex environments without requiring pre-existing models, offering substantial improvements over traditional control methods.
Applications
- Localized Drug Delivery
- Diagnosis and Biopsy
- Microsurgical Procedures
Advantages
This novel microrobot control system offers several critical advantages. It utilizes reinforcement learning to train artificial neural networks without requiring detailed pre-existing models of the microrobot's dynamics or the operating environment. This flexibility makes it highly adaptable to different medical applications and various in vivo conditions. Additionally, the use of machine learning enables the system to improve its performance over time, achieving better navigation accuracy and control as it learns from experience. The system's ability to function effectively without precise environmental models also reduces the complexity and cost of deployment in real-world scenarios. Furthermore, the integration of visual feedback and advanced electromagnet designs enhances the system's reliability and precision, making it a leading solution for next-generation medical microrobotics.
Invention Readiness
Researchers have created a functional prototype where artificial neural networks are trained to control helical magnetic microrobots in a fluidic channel, with visual feedback provided by an overhead camera. The microrobot's position is processed and transmitted to the neural network, which then generates control signals to drive electromagnets, guiding the microrobot through the desired trajectory. This system has been tested extensively, showing that it can autonomously navigate the microrobot without human intervention, using a reinforcement learning algorithm to optimize its performance. The system has demonstrated strong potential in laboratory settings and is poised for further development, including in vivo testing and commercialization efforts aimed at bringing this technology to clinical practice.
IP Status
https://patents.google.com/patent/US20240225764A1
