# Review of Advanced Medical Telerobots
## 3 Autonomy Levels
### Shared Control
#### 3.2.2 Shared Authority Blending-Based Control
[PERSON] and [PERSON] overcame this limitation in [266] with two architectures: the complementary linear combination (CLC) and the masters correspondence with environment transfer (MCET). In both architectures, master (leader) controllers are fed with reference signals that are linear combinations of desired velocities and feedback forces.For the CLC architecture, they are:  
\[ \begin{split} v_{m_{1_{d}}}&=\alpha v_{s}+(1- \alpha)v_{m_{2}},\;\;f_{m_{1_{d}}}=\alpha f_{e}+(1-\alpha)f_{m_{2}}\\ v_{m_{2_{d}}}&=(1-\alpha)v_{s}+\alpha v_{m_{1}},\; \;f_{m_{2_{d}}}=(1-\alpha)f_{e}+\alpha f_{m_{1}}\end{split}  \tag{3}\]  
where \(v_{m_{i}}\) is the velocity of master \(i\in\{0,1\}\), \(f_{m_{i}}\) the force applied by the user on master \(i\), \(v_{m_{i_{d}}}\) is the desired velocity for master \(i\in\{0,1\}\), \(f_{m_{i_{d}}}\) the desired feedback force for master \(i\), \(f_{e}\) the interaction force between the environment and the follower robot. For the MCET architecture, the desired feedback force is half of the environmental force for both users:  
Figure 9: Shared control based on authority management, in the specific case of a dual-user topology (Images ©2020 Intuitive Surgical, Inc.). \[v_{m_{1_{d}}}=v_{m_{2}},\ \ f_{m_{1_{d}}}=\frac{1}{2}f_{e} \tag{4}\] \[v_{m_{2_{d}}}=v_{m_{1}},\ \ f_{m_{2_{d}}}=\frac{1}{2}f_{e}\]  
Unfortunately, as visible in previous equations, both architectures feed users with a distorted tool-environment force. As a matter of fact, in numerous works, confusion for the user between the interaction forces (reflected from the follower robot interaction with the remote environment) and those generated for guidance and training is possible due to their mixing. In practice, in the low-level control, a force (or a torque) is generated by an actuator, and this force is generated so that the haptic device follows a combination of desired velocities and feedback force. It is then difficult for the user to differentiate the force generated to control the velocity and the feedback force during transient periods. During free motions, the force feedback typically corresponds to some guidance if any. During immobile interactions, as there every device is steady, the force feedback should reflect \(f_{e}\) (no follower interaction), Trouble in sensing correct force feedback may arise when switching between free motion and immobile interaction phases and during mobile interactions (while touching soft materials for instance). In this last case, feeling the right environment force requires the leader device to be moving at the exact desired velocity. As advised in [342], visual help should be provided to the \"following\" users to correctly position their device as the \"leading\" ones, during tool-environment contact phases to mitigate this challenge. Another linear-blending architecture is defined by [208], using an intrinsically passive architecture that enables both users to experience a full \(f_{e}\) force feedback. One can find other recent uses of linear blending for dual-users systems in [343; 344]. In practice, the authority blending should be ensured taking into account variable communication delays, packet loss, data duplication, and packet swapping [308], and actuator saturations [295]. The linear blending approach also increases efficiency; [PERSON] et al. demonstrated in [345] that a 2D-pattern cutting task with partial blood occlusion could result in a work time diminution compared to a fully manual task. This experiment highlighted the interest of a confidence-based shared control strategy with an adaptive blending. The aforementioned works on brain-interface for wheelchair navigation also make use of adaptive linear blending [325], which permits to dose the trajectory between the desired direction and the avoidance of obstacles. However, it remains important to leave some control on the blending to the users. Indeed, [PERSON] et al.