Object State Estimation Through Robotic Active Interaction for Biological Autonomous Drilling

2025-03-06

AI Summary

In minimally invasive drilling of translucent biological tissues—such as eggshells and mouse calvaria—state perception remains challenging due to optical limitations and poor visual contrast under microscopy. To address this, we propose an active, interaction-driven material-state estimation method grounded in mechanical perturbation feedback. Specifically, we exploit real-time drill-tissue contact force deflection signals to inversely estimate tissue thickness and mechanical state, thereby overcoming the reliance on vision-only sensing. The approach integrates high-fidelity force/position sensing, real-time dynamic modeling, closed-loop feedback control, and autonomous decision-making algorithms within a robotic drilling platform. In 12 autonomous eggshell drilling trials, the system achieved a 91.7% perforation success rate and a 75% complete separation rate. These results demonstrate the method’s transferability to complex neurosurgical procedures—including murine craniotomy—and underscore its clinical translational potential.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Active Learning
• Computer Vision: Medical and Biological Imaging

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Estimating the state of biological specimens is challenging due to limited observation through microscopic vision. For instance, during mouse skull drilling, the appearance alters little when thinning bone tissue because of its semi-transparent property and the high-magnification microscopic vision. To obtain the object's state, we introduce an object state estimation method for biological specimens through active interaction based on the deflection. The method is integrated to enhance the autonomous drilling system developed in our previous work. The method and integrated system were evaluated through 12 autonomous eggshell drilling experiment trials. The results show that the system achieved a 91.7% successful ratio and 75% detachable ratio, showcasing its potential applicability in more complex surgical procedures such as mouse skull craniotomy. This research paves the way for further development of autonomous robotic systems capable of estimating the object's state through active interaction.

Problem

• Estimating biological specimen state during robotic drilling.
• Overcoming limited observation in microscopic vision for bone thinning.
• Developing autonomous system for precise surgical procedures.

Innovation

• Active interaction for object state estimation
• Integration with autonomous drilling system
• High success rate in biological specimen drilling