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This paper presents the design, fabrication, and experimental validation of a photoacoustic (PA) imaging probe for robotic surgery. PA is an emerging imaging modality that combines the high penetration of ultrasound (US) imaging with high optical contrast. When equipped with a PA probe, a surgical robot can provide intraoperative guidance to the operating physician, alerting them of the presence of vital substrate anatomy (e.g., nerves or blood vessels) invisible to the naked eye. Our probe is designed to work with the da Vinci surgical system to produce three-dimensional PA images: We propose an approach wherein the robot provides remote center-of-motion (RCM) scanning across a region of interest, and successive PA tomographic images are acquired and interpolated to produce a three-dimensional PA image. To demonstrate the accuracy of the PA guidance in scanning 3D tomography actuated by the robot, we conducted an experimental study that involved the imaging of a multi-layer wire phantom. The computed Target Registration Error (TRE) between the acquired PA image and the phantom was 1.5567±1.3605 mm. The ex vivo study demonstrated the function of the proposed laparoscopic device in 3D vascular detection. These results indicate the potential of our PA system to be incorporated into clinical robotic surgery for functional anatomical guidance.

Autonomous navigation in dynamic environment heavily depends on the environment and its topology. Prior knowledge of the environment is not usually accurate as the environment keeps evolving in time. Since robot is continuously evaluating the environment as it proceeds, deciding the optimal way to traverse the environment to get to the goal, computationally efficient yet mathematically adaptive navigation algorithms are needed. In this paper, a navigation scheme for mobile robot, capable of dealing with time variant environment is proposed. This approach consists of a global planner (A*) and local planner (VFH) to assure an optimal and collisionfree robot motion. The algorithm is tested both in simulation and experimentation in different environments that are known to result in failures in VFH and ROS navigation stack, for comparison purposes. Overall, the algorithm enables the robot to get to the goal faster and also produces a smoother path while doing so.