Minimally invasive surgery (MIS) offers considerable advantages for patients, lowering infection risks, and reducing trauma and convalescence times. Dedicated surgical robots significantly improve surgeon's skills especially for tasks requiring high precision such as lead placement and suturing. However, these robotic setups do not allow yet beating heart surgery with motion compensation functionalities. This paper tackles autonomous heart motion compensation with force feedback. We propose a cascade model predictive control (MPC) architecture with a Kalman Active Observer (AOB) in the loop, and compare it with the classical MPC approach. The cascade MPC-AOB control architecture has two loops. The inner one performs model-reference adaptive control, guaranteeing a desired force tracking dynamics. The outer one generates control actions to compensate physiological motions. Both MPC-based architectures are analyzed and experimentally evaluated. Two robots are used. A lightweight 4-DoF surgical robot generates desired surgical forces and a 3-DoF robot equipped with an ex vivo heart at the end-effector reproduces realistic heart motions.

In beating-heart surgeries, two natural disturbances affect surgeon's skills: respiration and heartbeats. Heart motions are too dynamic to be manually compensated, therefore robotic-assisted surgery has the potential to improve surgical tasks, especially those requiring high precision such as needle insertion or suturing. In our approach only force data are used to autonomously compensate physiological motions. Two force control architectures are confronted and experimentally compared, using a lightweight 7 DOF WAM arm. One is based on linear predictive control, which uses a mathematical model to predict the system behavior. The other one is based on model reference adaptive control with stochastic active observers.

This paper presents a predictive force control approach to compensate for the physiological motion induced by both respiratory and heart beating motions during cardiac surgery. It focuses on the design and implementation of the control algorithm in the context of robotized minimally invasive surgery. The controller is based on a linear predictive control loop using the force information applied on the heart by the instrument. Experimental evaluation highlights the performance of the algorithm for compensating 3D physiological motion.

This paper proposes a new approach to compensate the physiological motion, induced by respiration and heart beating, for robotized minimally invasive cardiac surgery. The control algorithm, based on a linear predictive control, uses the effort information applied on the heart by the instrument.