# Review of Advanced Medical Telerobots
## 2 Medical Telerobotic Topologies
### Single-Leader Single-Follower Topology (SL/SF)
#### 2.1.2 Motor Augmentation through SL/SF Telebotic Surgery  
Besides sensory augmentation, telerobotic surgical systems are known to augment the motor performance of surgeons to directly correct and enhance the manipulations generated by the surgeon to minimize errors and increase the quality of surgery. In this regard, tremor compensation [2; 65; 66], organ motion compensation [75; 76], surgeon's motion scaling [2; 185; 186], guiding force fields [67; 68; 69; 70; 71], and forbidden virtual fixtures [67; 68; 111; 112; 187; 188] are existing examples of motor augmentation achieved using teleoperated robotic systems [1]. Regarding tremor compensation [65; 87; 189], it can be mentioned that through basic low pass filtering or in a more advanced manner through accurate estimation of hand tremor in realtime with minimum phase lag using advanced signal processing modules, such as band-limited multiple Fourier linear combiner [190; 191; 192; 193], it is now possible to extract and predict the high-frequency, low amplitude, involuntary tremorous motions of the surgeon's hand and damp them out before sending the motion to the surgical robot [1; 189]. Thus, through the computerized signal intervention achieved by leader-follower telerobotic systems, the tremorous motion of the surgeons can be converted to smooth ultra-fine motor commands, which increases the quality of delicate surgeries (in particular for microsurgeries such as in retinal operations) [66; 138; 139]. In addition to tremor compensation, using leader-follower teleoperated robotic systems, it is possible to scale down the motions generated by the surgeon [1; 2; 5; 185; 194]. Thiswould result in converting a very fine motor requirement to a course motor task for the surgeon. This would significantly increase the accuracy by reducing the complexity of control [5]. For example, the surgeon can move the leader robot accurately in centimeter ranges, while the motion can be scaled down to micrometer and can be replicated by micro-actuator in delicate surgeries. This framework is also valid for larger-scale operation such as laparoscopic operation when the surgeon can decide to scale down the motion to reduce the burden of fine motor control using telerobotic systems [194]. In the literature, motion scaling and tremor filtering are identified as two critical features of telerobotic surgical systems that enhance the dexterity and performance of surgeons when compared with manual surgeries [2; 5; 185]. Regarding organ motion compensation, it can be mentioned that through advanced computer vision and image processing modules (most of which have a Kalman filter core algorithm [11; 195; 196; 197; 198; 199]) it is now possible to measure motions of moving organs such as heart and lung during the operation and compensate automatically for the physiological motions while the surgeon can be responsible only for providing relative positional commands. The image modalities used for these applications are usually acquired by endoscopic cameras or ultrasound probes connected to the robotic tools [10; 11; 12; 13]. A major challenge for this motor augmentation task is the large latency for processing the images when compared with fast organ motions [11; 199; 190; 191; 192; 193; 194; 195; 196; 197; 198]. For example, generically, heart motion during surgery (after deceleration by medication) can be as high as 210 mm/s with an acceleration of 3800 mm/s\({}^{2}\)[199]. For addressing this issue advance and lightweight signal processing modules such as Kalman filter [11] and advanced control modules such as model predictive control [202] have been suggested in the literature and sometimes are fused with other modalities such as electrocardiograms (ECG) to track, predict and compensate for the phase lag [11; 199; 190; 191; 192; 193; 194; 195; 196; 197; 198; 199; 199; 200; 201; 202; 203; 204; 205]. This task is a form of shared autonomy (see Section 3.2) using which robots can utilize computer vision to compensate for the repetitive motions [12; 199; 203; 206; 207; 208].