# Review of Advanced Medical Telerobots
## 3 Autonomy Levels
### Shared Control
#### 3.2.1 Task Decomposition Based Shared Control  
The first category of shared control strategies, shown in Figure 8, lies on the task-decomposition approach, which consists of decomposing the task into several sub-tasks, where some of them are performed by the user, and others are automated for assistance purpose [313; 325; 326; 327; 16]. This decomposition can be performed by automating the most difficult part of the motions while leaving the operators free for safe motions and agency. A motivation for reducing their maneuverability is the decrease in their cognitive load. This approach is also called dimension reduction in [313], where the palpation task was decomposed into three chained sub-tasks: the robotic agent decides for the palpation locations, then the operator controls the palpation during the downward stroke until the force threshold is reached, and at which point, the robotic agent controls the upward palpation stroke. Thus, the operator is solely responsible for imparting forces. Another application is proposed in [327] where a flexible manipulator creates dynamic trajectory plans automatically. The surgeons only need to define the dissection trajectory with a few markers, while the system generates a trajectory based on the feedback from the stereo endoscope system, with consideration of the deformation of the tissues. Motion compensation is another application of task decomposition. For instance, the steady hand cooperative control was introduced at first in 1999 in [66]: a force sensor detects forces exerted by the surgeon on the leader handle, and the follower robot moves to provide smooth, tremor-free precise positional control and scaled force feedback. This medical application would be extremely difficult and unsafe (if even possible) for the patients without this assistance. For instance, in [328], the beating heart motion compensation is decomposed into three tasks; (1) motion synchronization, (2) image stabilization, and (3) shared control. In [211], for lung motion compensation during needle insertion, an impedance-based control was used. This approach has been enhanced in 2017 in [16] for tele-echography purposes where an impedance-controlled teleoperation system compensates for the natural motion of organs such as heart, chest movements uniquely via appropriate parameter adjustment in the desired impedance models, without requiring any direct measurement and/or online prediction of the organ's motions, and even in the presence of communication delays and modeling uncertainties. An enhanced version has been proposed in [329] and a multi-user version is available in [324] for training or cooperation purposes. In the particular case of multi-user training, the number of sub-tasks is increased proportionally. In this case, in addition to the aforementioned tasks, a new task adds position guidance to the trainees during the training procedure, and another one provides force feedback to the additional operators regardless of their levels of authority over the follower robot. Virtual fixture functions described in Section 2.1.2 provide guidance or inhibition feedback that is generated in a subtask synchronized with the operator's manual subtask. These features add safety and directional operations through software. For instance, in [326], a subtask is responsible for the online generation of some VFs using interaction force measurements, preoperative information (such as CT scans), and intraoperative information (such as body motion). A 2014 detailed survey about VFs is available in [67]. Traditionally camera control is conducted manually by the surgeon during the surgical workflow. An autonomous robotic system can help to decompose the controlling of tools and camera separately with minimum interruption of the continuous operation of surgical flow. In the past years, several shared control frameworks have been proposed by semi-autonomous systems for camera control.