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Robot-assisted femur repair has been of increased interest in recent literature due to the success of robot-assisted surgeries and current reoperation rates for femur fracture surgeries. The current limitation of robot-assisted femur fracture surgery is the lack of large force generation and sufficient workspace size in traditional mechanisms. To address these challenges, our group has created a 3-RRPS parallel mechanism, Robossis, which maintains the strength of parallel mechanisms while improving the translational and rotational workspace volume. In this paper, an optimal design methodology of parallel mechanisms for application to robot-assisted femur fracture surgery using a single-objective genetic algorithm is proposed. The genetic algorithm will use a single-objective function to evaluate the various configurations based on the clinical and mechanical design criteria for femur fracture surgery as well as the global conditioning index. The objective function is composed of the desired translational and rotational workspaces based on the design criteria, dynamic load-carrying capacity, and the homogeneous Jacobian global conditioning index. Lastly, experimental results of Robossis were obtained to validate the kinematic solution and the mechanism itself; Robossis had an average error of 0.31 mm during experimental force testing. 

This paper presents the experimental position and force testing of a 3-armed 6-DOF Parallel Robot, Robossis, that is specifically designed for the application of long-bone femur fracture surgery. Current surgical techniques require a significant amount of time and effort to restore the fractured femur fragments’ length, alignment and rotation. To address these issues, the Robossis system will facilitate the femur fracture surgical procedure and oppose the large traction forces/torques of the muscle groups surrounding the femur. As such, Robossis would subsequently improve patient outcomes by eliminating intraoperative injuries, reducing radiation exposure from X-rays during surgery and decreasing the likelihood of follow-up operations. Specifically, in this paper, we study the accuracy of the Robossis system while moving in the operational workspace under free and simulated traction loads of ([Formula: see text]–1100[Formula: see text]N). Experimental testing in this study demonstrates that Robossis can reach the most extreme points in the workspace, as defined by the theoretical workspace, while maintaining minimal deviation from those points with an average deviation of 0.324[Formula: see text]mm. Furthermore, the force testing experiment shows that Robossis can counteract loads that are clinically relevant to restoring the fractured femur fragments’ length, alignment and rotation. In addition, we study the accuracy of Robossis motion while coupled with the master controller Sigma 7. The results show that Robossis can follow the desired trajectory in real-time with an average error of less than 1[Formula: see text]mm. To conclude, these results further establish the ability of the Robossis system to facilitate the femur fracture surgical procedure and eliminate limitations faced with the current surgical techniques. 

A fundamental challenge in retinal surgery is safely navigating a surgical tool to a desired goal position on the retinal surface while avoiding damage to surrounding tissues, a procedure that typically requires tens-of-microns accuracy. In practice, the surgeon relies on depth-estimation skills to localize the tool-tip with respect to the retina and perform the tool-navigation task, which can be prone to human error. To alleviate such uncertainty, prior work has introduced ways to assist the surgeon by estimating the tool-tip distance to the retina and providing haptic or auditory feedback. However, automating the tool-navigation task itself remains unsolved and largely un-explored. Such a capability, if reliably automated, could serve as a building block to streamline complex procedures and reduce the chance for tissue damage. Towards this end, we propose to automate the tool-navigation task by mimicking the perception-action feedback loop of an expert surgeon. Specifically, a deep network is trained to imitate expert trajectories toward various locations on the retina based on recorded visual servoing to a given goal specified by the user. The proposed autonomous navigation system is evaluated in simulation and in real-life experiments using a silicone eye phantom. We show that the network can reliably navigate a surgical tool to various desired locations within 137 µm accuracy in phantom experiments and 94 µm in simulation, and generalizes well to unseen situations such as in the presence of auxiliary surgical tools, variable eye backgrounds, and brightness conditions.