As robot-assisted surgery is increasingly used in surgical care, the engineering research effort towards surgical automation has also increased significantly. Automation promises to enhance surgical outcomes, offload mundane or repetitive tasks, and improve workflow. However, we must ask an important question: should autonomous surgery be our long-term goal? To provide an overview of the engineering requirements for automating control systems, summarize technical challenges in automated robotic surgery, and review sensing and modeling techniques to capture real-time human behaviors for integration into the robotic control loop for enhanced shared or collaborative control. We performed a nonsystematic search of the English language literature up to March 25, 2021. We included original studies related to automation in robot-assisted laparoscopic surgery and human-centered sensing and modeling. We identified four comprehensive review papers that present techniques for automating portions of surgical tasks. Sixteen studies relate to human-centered sensing technologies and 23 to computer vision and/or advanced artificial intelligence or machine learning methods for skill assessment. Twenty-two studies evaluate or review the role of haptic or adaptive guidance during some learning task, with only a few applied to robotic surgery. Finally, only three studies discuss the role of some form of training in patient outcomes and none evaluated the effects of full or semi-autonomy on patient outcomes. Rather than focusing on autonomy, which eliminates the surgeon from the loop, research centered on more fully understanding the surgeon's behaviors, goals, and limitations could facilitate a superior class of collaborative surgical robots that could be more effective and intelligent than automation alone. We reviewed the literature for studies on automation in surgical robotics and on modeling of human behavior in human-machine interaction. The main application is to enhance the ability of surgical robotic systems to collaborate more effectively and intelligently with human surgeon operators.

Injuries affecting the central nervous system may disrupt neural pathways to muscles causing motor deficits. Yet the brain exhibits sensorimotor rhythms (SMRs) during movement intents, and brain–computer interfaces (BCIs) can decode SMRs to control assistive devices and promote functional recovery. However, noninvasive BCIs suffer from the instability of SMRs, requiring longitudinal training for users to learn proper SMR modulation. Here, we accelerate this skill learning process by applying cervical transcutaneous electrical spinal stimulation (TESS) to inhibit the motor cortex prior to longitudinal upper-limb BCI training. Results support a mechanistic role for cortical inhibition in significantly increasing focality and strength of SMRs leading to accelerated BCI control in healthy subjects and an individual with spinal cord injury. Improvements were observed following only two TESS sessions and were maintained for at least one week in users who could not otherwise achieve control. Our findings provide promising possibilities for advancing BCI-based motor rehabilitation.

The application of additive technologies, namely, fused deposition modeling, is a new reality for prototyping gripping devices of industrial robots. However, during 3D printing of holes and nozzle elements, difficulties arise with reducing their diameter. Therefore, this article conducts a comprehensive study of the Bernoulli gripping device prototype with a cylindrical nozzle, manufactured by fused deposition modeling 3D printing. The three main reasons for reducing the diameter of the gripper nozzle after printing were due to the poor-quality model, excessive extrusion of plastic in the middle of the arc printing path, and linear shrinkage of printing material after cooling. The proposed methodology consisted of determining the three coefficients that allowed the determination of the diameter of the designed nozzle. The use of air pressure distributions on the surface of the manipulation object, and lifting forces of gripping devices with different 3D printing layer heights were found. It was experimentally determined that as the height of the printing layer increased, the lifting force decreased. This was due to the formation of swirls due to the increased roughness of the grip surface. It was proven that as the height between the manipulation object and the grip increased, the effect of surface roughness on the lifting force decreased, resulting in an increase in the lifting force. Determination of the rational operating parameters of gripping devices manufactured by 3D printing from the point of view of maximum lifting force, were determined.

Gripping and manipulating objects using non-contact and low-contact technologies is becoming increasingly necessary in manufacturing. One of the promising contactless gripping technologies is Bernoulli gripping devices for industrial robots. They have many advantages, but when changing the nozzle geometry, it is difficult to find the optimal parameters of the outer diameter of the gripper and its operating parameters. Therefore, the article presents a model for numerical simulation of the dynamics of airflow in the nozzle of the Bernoulli gripping device and in the radial gap between its active surface and the surface of the object of manipulation. Reynolds-averaged Navier–Stokes equations of viscous gas dynamics, SST-model of turbulence, and γ-model of laminar-turbulent transition were used for this purpose. The technical requirements for the design of the nozzle of Bernoulli jet gripping nozzles are determined and variants of their constructive improvement are offered. According to the results of numerical simulation in the Ansys-CFD software environment, the optimal diameter of the Bernoulli gripping device and the influence of the geometric parameters of the nozzle on the nature of the pressure distribution in the radial gap and its lifting force were determined. Determined the optimal parameters of the height of the gap between the object of manipulation and the Bernoulli gripping device using C—Factor, which will allow efficient operation of Bernoulli gripping devices during automated handling operations using industrial robots.