# Review of Advanced Medical Telerobots
## 3 Autonomy Levels
### Shared Control
#### 3.2.2 Shared Authority Blending-Based Control
introduced in [340] a theoretical framework based on optimal control to optimize in real-time the linear blending according to users' preferences (i.e., own cost functions). It resulted that the amount of assistance was voluntarily lowered as some subjects favored retaining more control during the execution over better task performance. Nonlinear blending has been introduced by [PERSON] et al. by splitting the dominance into two factors \(\alpha\) and \(\beta\)[308]. Here, \(\alpha\) balances the authority between both users while \(\beta\) sets their supremacy over the follower robot. This provides an additional degree of freedom in the authority mechanism that is leveraged employing a nonlinear relation between \(\alpha\) and \(\beta\). Finally, only one parameter has to be set, which provides finer control of the authority. In the nonlinear blending category, we distinguish linear-blending based strategies where \(\alpha\) is determined using nonlinear mechanisms (we will refer to these solutions as \"nonlinear over linear\" solutions (NL/L), and full nonlinear solutions). In the NL/L category, [PERSON], et al. [206] propose an adaptive fuzzy-logic-based design to dynamically set \(\alpha\), according to the acquired expertise of the novice surgeon. It is evaluated on the fly from various measures by comparing the performance of the novice user with that of the expert. In [208; 346], the adaptive authority adjustment (AAA) function is proposed to automatically revert, in trainee evaluation mode, the authority from the trainee to the trainer in case of undesired behaviors of the trainee (as soon as the trainer deviates from the trainee's trajectory to rectify it). Following the same need of attributing the authority to the right user, the solution introduced in [347] reverts the authority to the trainer when he/she applies a force greater than a predefined one on his/her haptic device. In [348], in the context of surgical grasping, the robotic controller and the human inputs are linearly blended with a balance based on the computed confidence in the identification of the grasped object by an online identification of the grasped tissue. This strategy outperformed the regulation of the grasping forces to the desired target force compared to manual control. We cite here some authority blending solutions that are not based on the Equation (1) linear approach. In [247], adaptive blending is applied for robotics-assisted mirror rehabilitation therapy with the therapist-in-the-loop (TIL) approach, realizing a nonlinear Assist-as-Needed Therapy (ANT), see Section 3. A haptic-negotiation model that dynamically mixes the velocity and haptic feedback exchange between a wheelchair driver, an assistant, and the wheelchair follower controller is proposed in [349]. At first, a real human assistant trains a Gaussian process (GP) regression model that will then act as a virtual assistant. Authority blending with More than Two Users  
Authority blending has been extended to multilateral (ML/SF) topologies for generic purposes in [218], for the integration of a human assistant (forming so a trilateral topology, in [247; 349]), for cooperation purpose in beating-heart surgery (in the aforementioned [324]), and for training purposes with one (dual-user [218]) or several trainees (multi-user [342]) with real tool-environment force feedback simultaneously felt by every user. Multi-traine architectures allow several trainees, along with a trainer (potentially several ones) to use and learn on the same haptic training simulator (or real follower device) at the same time. This avoids the trainer to repeat the same gestures for each trainee. One trainee can perform a demonstration for evaluation purposes simultaneously towards the trainer and the other trainees, who can observe it, which can be interesting from a didactic point of view. In [218], a linear blending is proposed so that operators experience force feedback from the follower-tool-environment interaction force corresponding to their authority level.