Inspection planning, the task of planning motions that allow a robot to inspect a set of points of interest, has applications in domains such as industrial, field, and medical robotics. Inspection planning can be computationally challenging, as the search space over motion plans grows exponentially with the number of points of interest to inspect. We propose a novel method, Incremental Random Inspection-roadmap Search (IRIS), that computes inspection plans whose length and set of successfully inspected points asymptotically converge to those of an optimal inspection plan. IRIS incrementally densifies a motion-planning roadmap using a sampling-based algorithm, and performs efficient near-optimal graph search over the resulting roadmap as it is generated. We demonstrate IRIS's efficacy on a simulated planar 5DOF manipulator inspection task and on a medical endoscopic inspection task for a continuum parallel surgical robot in cluttered anatomy segmented from patient CT data. We show that IRIS computes higher-quality inspection plans orders of magnitudes faster than a prior state-of-the-art method.

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Lung cancer is the deadliest form of cancer, and early diagnosis is critical to favorable survival rates. Definitive diagnosis of lung cancer typically requires needle biopsy. Common lung nodule biopsy approaches either carry significant risk or are incapable of accessing large regions of the lung, such as in the periphery. Deploying a steerable needle from a bronchoscope and steering through the lung allows for safe biopsy while improving the accessibility of lung nodules in the lung periphery. In this work, we present a method for extracting a cost map automatically from pulmonary CT images, and utilizing the cost map to efficiently plan safe motions for a steerable needle through the lung. The cost map encodes obstacles that should be avoided, such as the lung pleura, bronchial tubes, and large blood vessels, and additionally formulates a cost for the rest of the lung which corresponds to an approximate likelihood that a blood vessel exists at each location in the anatomy. We then present a motion planning approach that utilizes the cost map to generate paths that minimize accumulated cost while safely reaching a goal location in the lung.

Lung cancer is the leading cause of cancer-related death, and early-stage diagnosis is critical to survival. Biopsy is typically required for a definitive diagnosis, but current low-risk clinical options for lung biopsy cannot access all biopsy sites. We introduce a motion planner for a multilumen transoral lung access system, a new system that has the potential to perform safe biopsies anywhere in the lung, which could enable more effective early-stage diagnosis of lung cancer. The system consists of three stages in which a bronchoscope is deployed transorally to the lung, a concentric tube robot pierces through the bronchial tubes into the lung parenchyma, and a steerable needle deploys through a properly oriented concentric tube and steers through the lung parenchyma to the target site while avoiding anatomical obstacles such as significant blood vessels. A sampling-based motion planner computes actions for each stage of the system and considers the coupling of the stages in an efficient manner. We demonstrate the motion planner's fast performance and ability to compute plans with high clearance from obstacles in simulated anatomical scenarios.

Concentric tube robots are thin, tentacle-like devices that can move along curved paths and can potentially enable new, less invasive surgical procedures. Safe and effective operation of this type of robot requires that the robot’s shaft avoid sensitive anatomical structures (e.g., critical vessels and organs) while the surgeon teleoperates the robot’s tip. However, the robot’s unintuitive kinematics makes it difficult for a human user to manually ensure obstacle avoidance along the entire tentacle-like shape of the robot’s shaft. We present a motion planning approach for concentric tube robot teleoperation that enables the robot to interactively maneuver its tip to points selected by a user while automatically avoiding obstacles along its shaft. We achieve automatic collision avoidance by precomputing a roadmap of collision-free robot configurations based on a description of the anatomical obstacles, which are attainable via volumetric medical imaging. We also mitigate the effects of kinematic modeling error in reaching the goal positions by adjusting motions based on robot tip position sensing. We evaluate our motion planner on a teleoperated concentric tube robot and demonstrate its obstacle avoidance and accuracy in environments with tubular obstacles.