In hands-on robotic surgery, the surgical tool is mounted on the end-effector of a robot and is directly manipulated by the surgeon. This simultaneously exploits the strengths of both humans and robots, such that the surgeon directly feels tool–tissue interactions and remains in control of the procedure, while taking advantage of the robot's higher precision and accuracy. A crucial challenge in hands-on robotics for delicate manipulation tasks, such as surgery, is that the user must interact with the dynamics of the robot at the end-effector, which can reduce dexterity and increase fatigue. This paper presents a null-space-based optimization technique for simultaneously minimizing the mass and friction of the robot that is experienced by the surgeon. By defining a novel optimization technique for minimizing the projection of the joint friction onto the end-effector, and integrating this with our previous techniques for minimizing the belted mass/inertia as perceived by the hand, a significant reduction in dynamics felt by the user is achieved. Experimental analyses in both simulation and human user trials demonstrate that the presented method can reduce the user-experienced dynamic mass and friction by, on average, 44% and 41%, respectively. The results presented robustly demonstrate that optimizing a robots pose can result in a more natural tool motion, potentially allowing future surgical robots to operate with increased usability, improved surgical outcomes, and wider clinical uptake.

In hands-on robotic surgery, the surgeon controls the motion of a tool mounted on the end effector by applying forces directly to the robot. The mass and inertia properties of the robot at the end effector thus contribute to the ability of the surgeon to move the tool and consequently, the performance of the surgery. As redundant robots have varying mass/inertia properties for different configurations at the same position and orientation of the end effector, we present optimizations which affect the inertial properties to improve the surgeon's movement capabilities. A method for optimizing the overall belted mass/inertia ellipsoids based on the determinant of the inverse pseudo kinetic energy matrices is presented, along with a method for optimizing the effective mass/inertia in a particular direction. Using a gradient based controller operating in the null-space of the end effector position and orientation, the measures are optimized to the local optima without affecting the surgeon's desired tool pose. Through simulation, the efficacy of the method is demonstrated and a comparison with two standard approaches to redundancy resolution is performed. Lastly, a pre-optimized solution is shown to be effective for heavily constrained environments which prevent active optimization.

Toward the goal of developing a hands-on robotic surgery control strategy which simultaneously utilizes the various strengths of both the surgeon and robot, we present a dynamic active constraint approach tailored for hands-on surgery. Forbidden region active constraints are used to prevent motion into areas which have been deemed dangerous by the surgeon, helping to overcome some of the disadvantages of fully active systems such as loss of tactile feedback, limited workspace, and limited field-of-view. The computer graphics technique of metaballs is used to represent point cloud data from an imaging system with an analytical, differentiable surface and a dynamics-based controller is proposed which controls the robot to lie on the zero set of the generated time-varying implicit function for which the motion is either known or unknown. This controller has been incorporated into a recursive null-space approach to allow for unimpeded motion along the surface and for further extension to joint optimization in the future. This methodology is demonstrated in simulation and on a lightweight, seven-degree-of-freedom serial manipulator.