Adding haptic feedback has been reported to improve the outcome of minimally invasive robotic surgery. In this study, we seek to determine whether an algorithm based on simulating responses of a cutaneous afferent population can be implemented to improve the performance of presenting haptic feedback for robot-assisted surgery. We propose a bio-inspired controlling model to present vibration and force feedback to help surgeons localize underlying structures in phantom tissue. A single pair of actuators was controlled by outputs of a model of a population of cutaneous afferents based on the pressure signal from a single sensor embedded in surgical forceps. We recruited 25 subjects including 10 expert surgeons to evaluate the performance of the bio-inspired controlling model in an artificial palpation task using the da Vinci surgical robot. Among the control methods tested, the bio-inspired system was unique in allowing both novices and experts to easily identify the locations of all classes of tumors and did so with reduced contact force and tumor contact time. This work demonstrates the utility of our bio-inspired multi-modal feedback system, which resulted in superior performance for both novice and professional users, in comparison to a traditional linear and the existing piecewise discrete algorithms of haptic feedback.

Density functional theory was utilized to assess the influence of In alloying on the spontaneous (Psp) and piezoelectric (Ppe) polarization of ε-Ga2O3 heterostructures with In concentrations ranging from 0% to 50%. The analysis demonstrated a decrease in both Psp and Ppe with an increase in In concentration, described by the equations Psp = −9.5947x + 24.81 and Ppe = −0.6217x (where x represents the In concentration, with units in μC/cm2). Additionally, the polarization-induced two-dimensional electron gas (2DEG) density within ε-InGaO/ε-Ga2O3 heterostructures was examined using a one-dimensional Schrödinger–Poisson solver. An inverse correlation was observed between 2DEG density and epitaxial thickness across all undoped In-alloyed samples. Furthermore, achieving high 2DEG densities (exceeding 1013 cm−2) is significantly facilitated by n-type doping concentrations above 1017 cm−3 in ε-InGaO. These insights not only augment the understanding of polarization effects in ε-Ga2O3 heterostructures but also provide a strategic framework for enhancing 2DEG density in ε-Ga2O3-based devices, which offers significant potential for advancing ε-Ga2O3-based high electron mobility transistors for power and RF applications.

Tactile information about an object can only be extracted from population responses of tactile receptors and their afferents. Thus, to best control tactile information in robots, neuroprostheses or haptic devices, inputs should represent responses from full populations of afferents. Here, we describe a simplified model that recreates afferent population responses of thousands of tactile afferents in a personal computer. The whole model includes a resistance network model to simplify the skin mechanics and an improved version of a single unit model that we have previously described. The whole model was implemented by short and efficient python code. The parameters of the model were fit based on a simple vibrating stimulus, but the simulated outputs generalize to match receptive field sizes, edge enhancement, and neurophysiological responses to dot textures, embossed letters and curved surfaces. We discuss how to use this work to model haptic perception and provide guidance in designing and controlling highly realistic tactile interfaces in robots, neural prostheses and haptic devices.