Method of force estimation for a minimally invasive medical system and corresponding system

1. A minimally invasive medical system, comprising:

a manipulator;

a detachable minimally invasive instrument having a first end and a second end; an effector unit connected to the manipulator, the effector unit configured to hold the first end of the detachable minimally invasive instrument;

a trocar configured to be mounted on a patient, wherein the second end of the instrument is configured to be positioned inside the patient through the trocar and beyond a fulcrum, the trocar configured to define said fulcrum which kinematically constraints the instrument;

a six-degrees-of-freedom force-torque sensor disposed between said effector unit and said manipulator and between said first end of the instrument and said manipulator, said sensor configured to measure forces and torques exerted onto said effector unit by said first end of the instrument;

a programmable computing device configured to:

determine an initial reference position of the detachable minimally invasive instrument relative to said fulcrum and to continuously update the position using manipulator motion information;

determine the position of said instrument relative to said fulcrum based on said determined initial reference position and on said continuously updated position;

and apply the principle of superposition to calculate an estimate of a force exerted onto the second end of said instrument based on said determined position, said measured force and said measured torque, wherein the measured force and measured torque used to calculate said estimate being only force and torque measured using the six-degrees-of-freedom force-torque sensor; and

provide haptic feedback signals to the manipulator based on said estimate.

2. The system as claimed in claim 1 , wherein the programmable computing device is further configured to apply the principle of superposition to calculate an estimate of a force exerted at said fulcrum by said instrument, based on said determined position, said measured force and said measured torque.

3. The system as claimed in claim 1 , wherein said effector unit is equipped with a six-degrees-of-freedom accelerometer configured to measure a gravity load or dynamic loads exerted onto said six-degrees-of-freedom force-torque sensor, and wherein the programmable computing device is further configured to process measurements made with said six-degrees-of-freedom accelerometer and to compensate said gravity or dynamic loads in said measured force and said measured torque.

4. The system as claimed in claim 1 , wherein said programmable computing device is further programmed to apply a linear Kalman filter to force and torque data measured with said six-degrees-of-freedom force/torque sensor prior to calculating said estimated force.

5. The system as claimed in claim 1 , wherein said programmable computing device is further programmed to apply a linear Kalman filter to said calculated force estimate.

6. The system as claimed in claim 3 , wherein said programmable computing device is further programmed to apply a primary linear Kalman filter to force and torque data measured with said six-degrees-of-freedom force-torque sensor and to linear and angular acceleration data measured with said six-degrees-of-freedom accelerometer; compensate disturbances due to gravity and dynamic loads after application of said primary linear Kalman filter; and apply a secondary linear Kalman filter to said compensated force and torque data.

7. The system of force estimation as claimed in claim 6 , wherein said linear Kalman filter is cascaded and has a first linear Kalman filter stage with a process noise covariance parameter set to a higher value in the range between 0.1 and 1, and a second linear Kalman filter stage with a process noise covariance parameter set to a lower value in the range between 0.001 and 0.1.

8. The system of force estimation as claimed in claim 7 , wherein at least one of said primary linear Kalman filter and said secondary linear Kalman filter is cascaded and has a first linear Kalman filter stage with a process noise covariance parameter set to a higher value in the range between 0.1 and 1, and a second linear Kalman filter stage with a process noise covariance parameter set to a lower value in the range between 0.001 and 0.1.