# Review of Advanced Medical Telerobots
## 3 Autonomy Levels
### Shared Control
#### 3.2.2 Shared Authority Blending-Based Control  
In this second category, the authority of control of the follower robot is distributed among the (real human or virtual) operators, with a balance that can vary in time. They share a complementary part of the control authority that is manually set on-line by one of the users or dynamically managed. Such authority blending is applied on multilateral teleoperation topologies (ML/SF and ML/MF, see Sections 2.3.1 and 2.3.3), out of them, dual-user topologies are the most studied ones (see Figure 9). Typical applications of dual-user haptic systems are for cooperation or training purposes, where both humans control the system through the shared control structure and are both provided with haptic feedback. It is also introduced as shared autonomy when a human and an autonomous system work together to achieve shared goals [340; 341]. We will distinguish linear and nonlinear blending approaches in this section in dual-user contexts and then in multilateral ones (more than two users). Considering the training application, the interest of haptics-enabled computer-based training systems for gesture training is that the follower is virtual (a simulation of the tools and the environment and their interactions) such that the trainees can rehearse as many times as necessary on the same exercise while being provided with objective assessment. Even if the aid from an experienced person can accelerate the training, this person can only guide the trainees \"from the outside of the simulation\", through a hand-over-hand guiding. As for traditional hands-on training, this approach does not permit both users to feel and dose the forces to apply to the tools as they share the same interaction. Dual-user training systems permit the trainers to get into the simulation (or even the manipulation of a real surgical robot), with force feedback for trainers and trainees. Shared control based on linear blending has been first introduced by [PERSON] et al., in 2005 in [268] for MIS telesurgical training, to allow for hands-on training of novice surgeons based on the skills of experts. It consists of a variable \(\alpha\in\mathbb{R}\) with \(\alpha\in[0,1]\), such that the follower robot velocity, \(v_{s}\), is a weighted sum of both user input velocity \(v_{m_{i}}\) with \(i\in\{1,2\}\) (\(m\) and \(s\) corresponding respectively to master (leader) and slave (follower)) such that:  
\[v_{s}=\alpha v_{m_{1}}+(1-\alpha)v_{m_{2}} \tag{1}\]Thus, when \(\alpha=1\) (resp. 0), user #1 (resp. #2) teleoperates the follower robot and user #2 (resp. #1) inputs do not affect the follower robot motion. When \(\alpha\in[0,1]\), the follower velocity reference is mixed according to Equation (1) so that both users share the leading of the follower according to the level of \(\alpha\). When \(\alpha=0.5\), both users' actions are equally balanced. There is no unified approach in the literature on the haptic feedback blending. In [268], the only feedback received by the users from the tool-environment interaction was visual, employing a remote camera image displayed on a local monitor. In practice, they fell in their hands a force proportional to the difference of position difference between them:  
\[ \begin{split} f_{m_{1}}&=K(1-\alpha)(x_{m_{2}}-x _{m_{1}})\\ f_{m_{2}}&=K\alpha(x_{m_{2}}-x_{m_{1}})\end{split}  \tag{2}\]  
where \(x_{m_{i}}\) and \(f_{m_{i}}\) are respectively the position and the force feedback of master (leader robot) \(i\), with \(i\in\{0,1\},K\in\mathbb{R}^{+}\). This provided the \"following\" user with some virtual force, guiding him/her toward the \"leading\" one, but it did not help novice users feel and learn the tool-environment interaction forces \(f_{e}\). Therefore, this kind of training system can only be used to train users on motions, not on efforts. The same limitation applies to [263] where the tool is virtual.