This paper presents a novel autonomous endoscope control method for the dVRK's Endoscopic Camera Manipulator (ECM), which allows the camera to track the surgical instruments on the Patient Side Manipulator (PSM). An Image-based Visual Servoing (IBVS) is enforced by the addition of a visibility constraint that ensures the identified surgical tool remains in the camera's Field Of View (FOV) for the continued availability of image feedback and a joint limits avoidance constraint that prevents the ECM from exceeding its joint limits. The work relies on an optimization approach, with constraints performed using the Control Barrier Functions concept (CBFs). The goal is to minimize the surgeon's cognitive and physical workload by removing the time-consuming job of camera reorientation, offering an enforced method compared to the traditional IBVS endoscopic camera controller.

Polyp dissection requires very accurate detection of the region of interest and high-precision cutting with adequate safety margins. Robot-assisted polyp dissection is a solution to accomplish high-quality intervention. This paper proposes a method to constrain the robot to follow an accurate dissection path based on Virtual Fixtures (VF). The VFs are created via specific control points obtained directly from images of the surgical scene and are updated by the vision algorithm. The VF constraints can autonomously adapt themselves to environment changing during the surgical intervention. The entire pipeline is validated through experiments on the da Vinci Research Kit (dVRK) robot.

During suturing tasks performed with minimally invasive surgical robots, configuration singularities and joint limits often force surgeons to interrupt the task and re-grasp the needle using dual-arm movements. This yields an increased operator's cognitive load, time-to-completion and performance degradation. In this paper, we propose a haptic-guided shared control method for grasping the needle with the Patient Side Manipulator (PSM) of the da Vinci robot avoiding such issues. We suggest a cost function consisting of (i) the distance from robot joint limits and (ii) the task-oriented manipulability along the suturing trajectory. Evaluating the cost and its gradient on the needle grasping manifold allows us to obtain the optimal grasping pose for joint-limit and singularity free robot movements during suturing. We compute force cues and display them through the Master Tool Manipulator (MTM) to guide the surgeon towards the optimal grasp. As such, our system helps the operator to choose a grasping configuration that allows the robot to avoid joint limits and singularities during post-grasp suturing movements. We show the effectiveness of the proposed haptic-guided shared control method during suturing using both simulated and real experiments. The results illustrate that our approach significantly improves the performance in terms of needle re-grasping.