Surgical robots equipped with cable-driven mechanisms have flexible, light, and compact arms and tools. However, cable slack, stretch, and gear backlash introduce unavoidable errors from motor positions to joint positions and the end-effector pose. This paper presents a learning-based joint position calibration method for the RAVEN-II surgical robot, employing deep neural networks and gated recurrent units. Compared to fixed offset compensation, the learning-based calibrations reduce the joint position errors by over 62.4% (unloaded) and 54.8% (loaded). Furthermore, removal and inaccurate ablation studies on input features identify that raw joint positions and motor torques are the most important model inputs for calibration accuracy. These studies also reveal that the models are capable of inferring joint positions from the end-effector pose and prioritize the direction of motor torques over their amplitude. When guided appropriately, the models can also compensate for encoder value inconsistencies occurring with robot re-homings. By excluding the unnecessary input features, lightweight models are developed and achieve better performance and efficiency simultaneously, reducing the training time on the CPU to 2.5 minutes. All data and code are open-source at https://github.com/uw-biorobotics/RAVEN-2-Feature-Ablation.

The International Space Station (ISS) serves as a research lab for a wide variety of experiments including some that study the biological effects of microgravity and spaceflight using the Rodent Habitat and Microgravity Science Glovebox (MSG). Astronauts train for onboard dissections of rodents following basic training. An alternative approach for conducting these experiments is teleoperation of a robot located on the ISS from earth by a scientist who is proficient in rodent dissection. This pilot study addresses (1) the effects of extreme time delay on skill degradation during Fundamentals of Laparoscopic Surgery (FLS) tasks and rodent dissections using RAVEN II; (2) derivation and testing of rudimentary interaction force estimation; (3) elicitation of design requirements for an onboard dissection robot, RAVEN-S; and (4) simulation of the RAVEN-S prototype design with dissection data. The results indicate that the tasks' completion times increased by a factor of up to 9 for a 3 s time delay while performing manipulation and cutting tasks (FLS model) and by a factor of up to 3 for a 0.75 s time delay during mouse dissection tasks (animal model). Average robot forces/torques of 14N/0.1Nm (peak 90N/0.75Nm) were measured along with average linear/angular velocities of 0.02m/s/4rad/s (peak 0.1m/s/40rad/s) during dissection. A triangular configuration of three arms with respect to the operation site showed the best configuration given the MSG geometry and the dissection tasks. In conclusion, the results confirm the feasibility of utilizing a surgically-inspired RAVEN-S robot for teleoperated rodent dissection for successful completion of the predefined tasks in the presence of communications time delay between the ISS and ground control.

Surgical robot research is driven by the desire of improving surgical outcomes. This paper proposed a Recurrent Neural Network based controller to address two problems: 1) improving control precision, 2) increasing adaptiveness for robot motion (explained in Section I). RNN was adopted in this work mainly because 1) the problem formulation naturally matches RNN structure, 2) RNN has advantages as an biologically inspired method. The proposed method was explained in detail and analysis shows that the proposed method is able to dynamically regulate outputs to increase the adaptiveness and the control precision. This paper uses Raven II surgical robot as an example to show the application of the proposed method, and the numeral simulation results from the proposed method and three other controllers show that the proposed method has improved precision, improved high robustness against noise and increased movement smoothness, and it keeps the manipulator links as far away as possible from physical boundaries, which potentially increases surgical safety and leads to improved surgical outcomes.

In this paper we present experimentally obtained cable-pulley Coulomb and viscous friction for cable-driven surgical robotic systems including the RAVEN II surgical robotic research platform. In the study of controlling cable-driven systems a simple mathematical model which does not capture physical behavior well is often employed. Even though control of such systems is achievable without an accurate model, fully understanding the behavior of the system will potentially realize more robust control. A surgical robot is one of the systems that often relies on cables as an actuation method as well as pulleys to guide them. Systems with such structure encounter frictional force related to conditions of cable and pulley such as cable velocity, tension, type and number of pulley, and angle of cable wrapping around pulley. Using a couple of test platforms that incorporate cable, pulleys, and other experimental conditions corresponding to the RAVEN II system, it is shown that cable-pulley friction is function of tension, wrap angle, and number of pulleys and not of magnitude of cable velocity.

Using an accelerometer on the base of a robot, it is possible to calculate the torque required from each actuator in order to maintain a known pose regardless of base orientation with respect to the direction or magnitude of gravity. A simple and novel method has been developed and implemented for overcoming gravity induced torques on the RAVENTMsurgical research robot. This innovation will allow for accurate control of serial robot manipulators with re-orientable bases or for those operating in non-stationary environments such as boats, space stations, or moving vehicles.