Stiffness-observation-based force feedforward compensation control for interactive robot-assisted surgical bone milling

Abstract

Robot-assisted surgery encounters critical force control challenges during risky operations like craniotomy skull milling, where collaborative operation demands adaptation to three surgical-specific complexities: multi-scale stiffness variations across biological tissues, abrupt stiffness discontinuities at critical boundaries (e.g. skull-dura interface), and unintuitive operator inputs during human-robot interaction. Consequently, controllers must dynamically adapt to this wide spectrum of tissue properties, a capability which exceeds the limits of conventional compliance control frameworks. This work presents a stiffness-observation-based force feedforward compensation controller that monitors the force-feedrate differential relationship to estimate real-time tissue stiffness, discriminating tissue types while compensating real-time force controllers. This controller is integrated into an active-constrained framework, replacing compliance control in the depth direction during milling operations. It establishes a hierarchical force control architecture where stiffness-derived information autonomously steers safety strategies, while surgeon-defined force constraints enable shared autonomy in human-robot interaction. The controller is numerically validated in simulated surgical environments and experimentally tested via in vivo craniotomies, demonstrating effective force tracking and safety assurance during complex milling tasks. By converting stiffness observations into real-time control actions, this approach enhances surgical safety in bone-tissue boundary transitions while maintaining intuitive human-robot collaboration.