Teleoperation | efi robotics

Bilateral teleoperation with haptic feedback for minimally invasive surgery.

Abstract

A desktop haptic device is used to teleoperate an industrial redundant and compliant robotic arm with a surgical instrument mounted on its end-effector. The master and slave devices are coupled in a bilateral position-position architecture. Force feedback is provided by the master haptic device to the user, from the position of the slave’s wrist. A surgical task (palpation) that involves force feedback is presented and tested in a user study with surgeons and non-medical participants. Results show that users easily discern between three different materials during palpation given minimal familiarisation time. Active constraint enforcement is also integrated with the system as a sensitive area around the palpation samples which the slave instrument is prohibited to enter {& cite Psomopoulou2020 %}.

Key points

A position-position control architecture is utilised to provide force feedback where both robots are controlled to track each other’s TCP.
Substantially different master and slave robots (in terms of size and dynamics).
Palpation of Soft Tissue-Like Materials.
Application of Active Constraints Enforcement.

Slave arm (KUKA iiwa) touching a virtual surface (left) and real soft silicon materials (right).

Video

Project scope

This work was part of the SMARTsurg project. Other publications include: [1], [2], [3], [4], [5].

References

MEDICON

Control of a da Vinci EndoWrist Surgical Instrument Using a Novel Master Controller

Sajeeva Abeywardena, Efi Psomopoulou, Mohammad Fattahi Sani, Antonia Tzemanaki, and Sanja Dogramadzi

In XV Mediterranean Conference on Medical and Biological Engineering and Computing, Jun 2020

Abs DOI HTML PDF

A novel master controller for robot-assisted minimally invasive surgery (RAMIS) is introduced and used to control a da Vinci EndoWrist instrument. The geometric model of the master mechanism and its mapping to the geometry of the EndoWrist tool are derived. Experimental results are conducted to open and close the jaws of an EndoWrist tool, and show that the developed mapping algorithm is accu- rate with a root mean square error of 0.7463 mm.
MEDICON

Towards Finger Motion Tracking and Analyses for Cardiac Surgery

Mohammad Fattahi Sani, Sajeeva Abeywardena, and Efi Psomopoulou

In XV Mediterranean Conference on Medical and Biological Engineering and Computing, Jun 2020

Abs DOI HTML PDF

Robot Assisted Surgery is attracting increasing amount of attention as it offers numerous benefits to patients as well as surgeons. Heart surgery requires a high level of precision and dexterity, in con- trast to other surgical specialties. Robot assisted heart surgery is not as widely performed due to numerous reasons including a lack of appropri- ate and intuitive surgical interfaces to control minimally invasive surgi- cal tools. In this paper, finger motion of the surgeon is analyzed during cardiac surgery tasks on an ex-vivo animal model with the purpose of designing a more intuitive master console. First, a custom finger tracking system is developed using IMU sensors, which is lightweight and com- fortable enough to allow free movement of the surgeon’s fingers/hands while using instruments. The proposed system tracks finger joint angles and fingertip positions for three involved fingers (thumb, index, mid- dle). Accuracy of the IMU sensors has been evaluated using an optical tracking system (Polaris, NDI). Finger motion of the cardiac surgeon while using a Castroviejo instrument is studied in suturing and knot- ting scenarios. The results show that PIP and MCP joints have larger Range Of Motion (ROM), and faster rate of change compared to other finger/thumb joints, while thumb has the largest Fingertip WorkSpace (FWS) of all three digits.
FRONT

Estimation of Tool-Tissue Forces in Robot-Assisted Minimally Invasive Surgery Using Neural Networks

Sajeeva Abeywardena, Qiaodi Yuan, Antonia Tzemanaki, Efi Psomopoulou, Leonidas Droukas, Chris Melhuish, and Sanja Dogramadzi

Frontiers in Robotics and AI, Jul 2019

Abs DOI HTML PDF

A new algorithm is proposed to estimate the tool-tissue force interaction in robot-assisted minimally invasive surgery which does not require the use of external force sensing. The proposed method utilises the current of the motors of the surgical instrument and neural network methods to estimate the force interaction. Offline and online testing is conducted to assess the feasibility of the developed algorithm. Results showed that the developed method has promise in allowing online estimation of tool-tissue force and could thus enable haptic feedback in robotic surgery to be provided.
CRAS

Incision Port Displacement Modelling Verification in Minimally Invasive Surgical Robots

Iman Sayyaddelshad, Efi Psomopoulou, Sajeeva Abeywardena, Antonia Tzemanaki, and Sanja Dogramadzi

In Joint workshop on New Technologies for Computer/Robot Assisted Surgery, Jul 2018

PDF
CRAS

Using current measurement to estimate palpation and grasping forces in robot-assisted minimally invasive surgery

Antonia Tzemanaki, Sajeeva Abeywardena, Efi Psomopoulou, Chris Melhuish, and Sanja Dogramadzi

In Joint workshop on New Technologies for Computer/Robot Assisted Surgery, Jul 2018

PDF