Achieving Dexterous Bidirectional Interaction in Uncertain Conditions for Medical Robotics

TL;DR

Medical robots must safely interact with patients under variable tissue properties and communication delays. This work evaluates a Fractal Impedance Controller (FIC) based architecture that is passivity-based, robust to delays up to $1\,\text{s}$, and capable of online admittance-impedance switching, enabling dexterous teleoperation across surgery, rehabilitation, and diagnostics. The approach supports modular, tuning-free operation and multi-arm coordination via SEIKO Retargeting, with experiments demonstrating scalpel cutting, ultrasound scanning, rehabilitation, and bimanual telemanipulation; limitations include 3D perception and embodiment that require improvement for clinical readiness. Overall, the findings suggest that the FIC framework provides robust, adaptable teleoperation for medical robotics, with practical impact in expanding access to therapy and remote diagnostics, pending enhancements in perception and haptic fidelity.

Abstract

Medical robotics can help improve and extend the reach of healthcare services. A major challenge for medical robots is the complex physical interaction between the robot and the patients which is required to be safe. This work presents the preliminary evaluation of a recently introduced control architecture based on the Fractal Impedance Control (FIC) in medical applications. The deployed FIC architecture is robust to delay between the master and the replica robots. It can switch online between an admittance and impedance behaviour, and it is robust to interaction with unstructured environments. Our experiments analyse three scenarios: teleoperated surgery, rehabilitation, and remote ultrasound scan. The experiments did not require any adjustment of the robot tuning, which is essential in medical applications where the operators do not have an engineering background required to tune the controller. Our results show that is possible to teleoperate the robot to cut using a scalpel, do an ultrasound scan, and perform remote occupational therapy. However, our experiments also highlighted the need for a better robots embodiment to precisely control the system in 3D dynamic tasks.

Figures (11)

• Figure 1: On the master side, there are the operator PC and the haptic feedback devices (Sigma.7, Force Dimension Inc.). On the Replica side, 7-dof torque-controlled arms (Panda, Franka Emika GmbH) are tested in scenarios targeting surgery, rehabilitation, and diagnostics. The controller of the master has three elements. $\text{T}_\text{M}$ is the module that transforms the motion of the master ($\bm{x}_\text{M}$) in the desired pose for the replica ($\bm{x}_\text{d}$). $\text{C}_\text{M}$ is a controller providing virtual haptic feedback ($h_\text{M}$) to provide additional information to the user (e.g., workspace boundaries). $\text{K}_\text{H}\in [0,1] \subset \mathbb{R}$ is the gain applied to the wrench recorded at the end-effector of the replica robots ($h_\text{e}$). The controller of the replica has two elements. $\text{FC}$ is the force controller that can be turned on when required, introducing an admittance controller on top of the low-level Interaction Controller (IC). $\text{MA \& IC}$ is a module composed of two components. The first element is the Motion Adaptation (MA) performed by an S-QP optimisation to guarantee that the desired trajectory respects the physical limitation of the robot (e.g., power limits and singularities) and the task (e.g., holding an object in bimanual manipulation). The second element is the IC that generates the torque commands to track the desired motion produced by the MA. It is worth remarking that in our experiments, the patients are substituted by two phantoms and a researcher, and another researcher acts as medical personnel.
• Figure 2: The proposed method has been used in multiple applications just by changing the end-effectors without requiring controller tuning. a) The hand end-effector used to hold the phantom during the cutting is mounted on the left arm and the support for the scalpel is on the right arm. b) The right arm has been equipped with a brace that is secure to the subject's arm with velcro straps. c) A vice-like end-effector is mounted on the right arm to secure the ultrasound probe to the robot. d) Two TACTIP sensors developed from the Bristol Robotics Laboratory pestell2019sense have been mounted on the two robots to enable the bimanual telemanipulation of the potato chip.
• Figure 3: Operator point of view for the scalpel and rehabilitation experiments.
• Figure 4: a) The cut marks on the silicone phantom show that it is difficult to proceed on a straight line. In addition, the deviation has peaks of a few millimetres, indicating the need to improve the system's performance on this task. b) The margins of the cut marks are needed, showing that the robot can robustly sustain contact with the phantom during the incision.
• Figure 5: The force data for the scalpel experiments show that the robots are capable of sufficient force to hold the phantom down during cutting and can safely pass the peaks of force encountered during the cutting on the scalpel. The last two trials were conducted to check the performance in executing cross-cutting tests, and they were executed without changing the controller's parameters.