State estimation and feedforward tremor suppression for a handheld micromanipulator with a Kalman filter

Abstract

Abstract-Active compensation of physiological tremor for handheld micromanipulators depends on fast control and actuation responses. Because of real-world latencies, real-time compensation is usually not completely effective at eliminating unwanted hand motion. By modeling tremor, more effective cancellation is possible by anticipating future hand motion. We propose a feedforward control strategy that utilizes tremor velocity from a state-estimating Kalman filter. We demonstrate that estimating hand motion in a feedforward controller overcomes real-world latencies in micromanipulator actuation. In hold-still tasks with a fully handheld micromanipulator, the proposed feedforward approach improves tremor rejection by over 50%.

Vision-Based Control of a Handheld Surgical Micromanipulator With Virtual Fixtures

“…Recently we proposed a virtual fixture framework for handheld micromanipulators that integrates both tremor compensation and motion scaling [12]. This paper expands upon that work by formulating a more general spline representation for virtual fixtures, integration of vision-based control with dense stereo vision, a new feedforward controller derived from [13], and extensions from planar surfaces to naturally curved surfaces. Additional evaluation, including a vein-tracing to mimic canulation and an extension of the membrane peeling experiment of [14], are explored.…”

“…However, the remaining 10% of tremor represents 10–20 μm deviations of the tip position from the goal, making it the largest contributing factor in the error reported for hard virtual fixtures [12]. Analysis revealed that a 3-ms latency in the Micron manipulator plant G ( s ) is largely responsible for tip positioning error [13]. We denote T as the latency between when the ASAP controller C ( s ) sends a command to the Micron manipulator G ( s ) and when the effect is seen in the tip position.…”