# Review of Advanced Medical Telerobots
## 3 Autonomy Levels
### Shared Control
#### 3.2.1 Task Decomposition Based Shared Control
A review of the state of the art of autonomous camera control systems in surgical applications is conducted in [330], in which various techniques are introduced for autonomous control of the camera based on eye-gaze tracking, instrument position in space, and image-Based scene tracking. For example, eye gaze tracking is used to automatically center the viewpoint of a laparoscopic camera regarding the user's point of gaze in [331]. As another relevant topic, path planning methods for the camera control have also been evaluated in the literature, such as an algorithm based on rapidly exploring random tree (RRT) [332]. The development of autonomous camera control of the da Vinci Surgical System is investigated in [333]. It can generate autonomously-centered and zoomed viewpoints by keeping the surgical tools in the camera's field of view. The aforementioned technique has shown reduced user-perceived workload and increased efficiency, and progress. Besides, it should be noted that autonomous camera control can be very beneficial for the initial stages of surgical training, when the trainee is becoming familiar with robotic surgery, and thus simplifying the control can be significantly beneficial for the early stages of training. In this regard, the autonomous camera navigation during the robotic surgical training is investigated in [334] where the experimental evaluations suggested improved performances and efficiency of training with robot-assisted surgery. In summary, it can be mentioned that autonomous control of the camera can be considered as one modality of shared control (between the machine and the operator) to reduce the cognitive workload, enhancing ease-of-use and providing more safety and consistency of surgery. Task decomposition for a human-machine shared control strategy is also used in various medical applications such as the control of a wheelchair [325]. In this case, a brain-machine interface using steady-state visual evoked potentials is introduced to guide the wheelchair while a vision-based algorithm provides simultaneous localization and mapping (SLAM) to help with navigation among the obstacles. Another relevant application is  
Figure 8: Shared control based on task decomposition, main principle (Images ©2020 Intuitive Surgical, Inc.). myoelectric control of prostheses [335; 336], which allows users to recover lost functionality by controlling a prosthetic robotic device with their remaining muscle activity. In [337], computer vision (for autonomous object recognition) and mechanomyography (to estimate the intended muscle activation) data are fused to conduct a shared control that predicts user intent for grasp and then realizes it. In [338], once the user establishes a pre-contact between the robotic hand and an object (manual task), the shared controller optimizes the actuation of the fingers of a robotic hand to maximize the contact between the hand and object to obtain full-contact (robotic task). In 2018, [PERSON] et al. proposed a shared-control based solution to perform semi-autonomous suturing with two robotic arms. In this procedure, the operator inserts the needle into an organ using one of the follower robots when follower A1 was directly controlled through a bilateral teleoperation architecture and follower A2 automatically grasps the tip of the needle and pulls it out from the organ, and automatically hands the needle back to follower A1. The operator repeats the same for each throw. An estimation of the force involved in the interaction of A1 with the organ is used to trigger the start of the A2 sub-task. This way, the completion time was decreased by an average of 20% in total. This is an interesting mixture of autonomy levels. Ref. [339] highlights that finding the point of puncture or holding the needle are activities that require some adaptation or manual correction by the surgeon, with risks of incorrect suturing and subsequently postoperative complications (calling for more advanced research related to autonomy).