Surgical automation has the capability to improve the consistency of patient outcomes and broaden access to advanced surgical care in underprivileged communities. Shared autonomy, where the robot automates routine subtasks while the surgeon retains partial teleoperative control, offers great potential to make an impact. In this paper we focus on one important skill within surgical shared autonomy: Automating robotic assistance to maximize visual exposure and apply tissue tension for dissection and cautery. Ensuring consistent exposure to visualize the surgical site is crucial for both efficiency and patient safety. However, achieving this is highly challenging due to the complexities of manipulating deformable volumetric tissues that are prevalent in surgery. To address these challenges we propose MEDiC, a framework for autonomous surgical robotic assistance to Maximizing Exposure for Dissection and Cautery. We integrate a differentiable physics model with perceptual feedback to achieve our two key objectives: 1) Maximizing tissue exposure and applying tension for a specified dissection site through visual-servoing control and 2) Selecting optimal control positions for a dissection target based on deformable Jacobian analysis. We quantitatively assess our method through repeated real robot experiments on a tissue phantom. Our visual-servoing and optimal control position selection achieve success rate of 100% and 82% respectively in ablation study. We also showcase our framework's capabilities through dissection experiments using shared autonomy on real animal tissue.

Controlling robotic manipulators via visual feedback requires a known coordinate frame transformation between the robot and the camera. Uncertainties in mechanical systems as well as camera calibration create errors in this coordinate frame transformation. These errors result in poor localization of robotic manipulators and create a significant challenge for applications that rely on precise interactions between manipulators and the environment. In this work, we estimate the camera-to-base transform and joint angle measurement errors for surgical robotic tools using an image based insertion-shaft detection algorithm and probabilistic models. We apply our proposed approach in both a structured environment as well as an unstructured environment and measure to demonstrate the efficacy of our methods.

Inaccurate tool localization is one of the main reasons for failures in automating surgical tasks. Imprecise robot kinematics and noisy observations caused by the poor visual acuity of an endoscopic camera make tool tracking challenging. Previous works in surgical automation adopt environment-specific setups or hard-coded strategies instead of explicitly considering motion and observation uncertainty of tool tracking in their policies. In this work, we present SURESTEP, an uncertainty-aware trajectory optimization framework for robust surgical automation.We model the uncertainty in tool tracking by considering noise sources that are typical in surgical environments.Using a Gaussian assumption to propagate our uncertainty models through a given tool trajectory, SURESTEP provides a general framework that minimizes the upper bound on the entropy of the final estimated tool distribution.We showcase our method by performing the first-ever, to our knowledge, needle regrasping with a moving endoscopic camera.We compare SURESTEP with a baseline method on a real-world suture needle regrasping task under challenging environmental conditions, such as poor lighting and a moving endoscopic camera. The results over 60 regrasps on the da Vinci Research Kit (dVRK) demonstrate that our optimized trajectories significantly outperform the un-optimized baseline.

There has been increasing awareness of the difficulties in reaching and extracting people from mass casualty scenarios, such as those arising from natural disasters. While platforms have been designed to consider reaching casualties and even carrying them out of harm's way, the challenge of repositioning a casualty from its found configuration to one suitable for extraction has not been explicitly explored. Furthermore, this planning problem needs to incorporate biomechanical safety considerations for the casualty. Thus, we present a first solution to biomechanically safe trajectory generation for repositioning limbs of unconscious human casualties. We describe biomechanical safety as mathematical constraints, mechanical descriptions of the dynamics for the robot-human coupled system, and the planning and trajectory optimization process that considers this coupled and constrained system. We finally evaluate our approach over several variations of the problem and demonstrate it on a real robot and human subject. This work provides a crucial part of search and rescue that can be used in conjunction with past and present works involving robots and vision systems designed for search and rescue.

In the past several years, significant research has been conducted in the area of real-time emotion recognition. Emotion recognition has several potential applications in education, medicine, assistive technologies and human-machine interaction. A real-time emotion detection device that utilizes heart rate and skin conductance sensors is presented in this paper. OpenCV, open face libraries and insight SDK is utilized to detect emotions from facial expressions. The performance of the device is evaluated using experiments which had subjects watch audiovisual clips in various emotional categories. Also, in order to verify the feasibility of utilizing bio-signals to predict emotions, facial expressions captured from a webcam are processed in parallel to compare and contrast.

The Brain-computer interface (BCI) is an engaging field which could find applications in numerous fields like industrial, biomedical and engineering. In this paper a BCI based electric wheelchair driving aid design that utilizes mental concentration (EEG signals) and eye blinks (EMG signals) of the user, is presented. The design incorporates a safety controller with peripheral safety sensors that override the user command and stop the wheelchair when it detects an obstacle in its path. The wheelchair driving aid design is cost-effective (estimated cost less than $200) as it utilizes off-the-shelf BCI headset and electronics. Four experiments were conducted to validate the performance and reliability of the design.