This paper investigates automation of soft tissue compression for robot-assisted surgery. This is a fundamental task in surgery and includes interaction with a variety of tissues with unknown properties. In addition, due to sterilization and size constraints the use of contact force and position sensors are often avoided in surgical applications. We propose an Adaptive Model Predictive Control approach for execution of given tool trajectories in contact with unknown tissues in the absence of contact measurements. The Unscented Kalman Filter is employed in advance of system operation to identify the dynamics of a cable driven manipulator. These dynamics are then used to estimate contact force and position in free motion and in contact with tissue. An optimal control problem for automating tissue compression is formulated and is solved in real-time using Differential Dynamic Programming with Automatic Differentiation. The proposed methods are evaluated in experiments on an artificial tissue sample with unknown properties.

Cable driven power transmissions are used in applications such as haptic devices, surgical robots etc. The use of flexible cable based power transmission often causes relative motion between the motor actuator and mechanism joint during operation due to the elasticity of the cable. State-space control methods can be used to improve performance, but may require state estimates. For nonlinear systems, the Unscented Kalman Filter (UKF) provides a computationally efficient way to obtain state estimates. The UKF is applied here to a simulation of a minimially invasive surgical robot, to study the state estimation for a cable driven system with nonlinear dynamics. State estimates from the UKF are compared with the known states available from the simulation. These state estimates are also utilized by two different controllers interacting with the simulation to test the UKF performance under closed loop control. We tested the UKF performance with error perturbations in the system model's cable stiffness parameter.

This paper proposes a novel algorithm for haptic rendering from time varying point clouds captured using an Xbox Kinect RGB-D camera. Existing methods for point-based haptic rendering using a proxy can not directly be applied in this situation since no information about the underlying objects is given. This paper extends the notion of proxy to point clouds. The haptic algorithm presented here renders haptic forces from point clouds captured in real-time representing both static and dynamic objects.