Medical steerable needles can follow 3D curvilinear trajectories inside body tissue, enabling them to move around critical anatomical structures and precisely reach clinically significant targets in a minimally invasive way. Automating needle steering, with motion planning as a key component, has the potential to maximize the accuracy, precision, speed, and safety of steerable needle procedures. In this paper, we introduce the first resolution-optimal motion planner for steerable needles that offers excellent practical performance in terms of runtime while simultaneously providing strong theoretical guarantees on completeness and the global optimality of the motion plan in finite time. Compared to state-of-the-art steerable needle motion planners, simulation experiments on realistic scenarios of lung biopsy demonstrate that our proposed planner is faster in generating higher-quality plans while incorporating clinically relevant cost functions. This indicates that the theoretical guarantees of the proposed planner have a practical impact on the motion plan quality, which is valuable for computing motion plans that minimize patient trauma.

Steerable needles are medical devices with the ability to follow curvilinear paths to reach targets while circumventing obstacles. In the deployment process, a human operator typically places the steerable needle at its start position on a tissue surface and then hands off control to the automation that steers the needle to the target. Due to uncertainty in the placement of the needle by the human operator, choosing a start position that is robust to deviations is crucial since some start positions may make it impossible for the steerable needle to safely reach the target. We introduce a method to efficiently evaluate steerable needle motion plans such that they are safe to variation in the start position. This method can be applied to many steerable needle planners and requires that the needle's orientation angle at insertion can be robotically controlled. Specifically, we introduce a method that builds a funnel around a given plan to determine a safe insertion surface corresponding to insertion points from which it is guaranteed that a collision-free motion plan to the goal can be computed. We use this technique to evaluate multiple feasible plans and select the one that maximizes the size of the safe insertion surface. We evaluate our method through simulation in a lung biopsy scenario and show that the method is able to quickly find needle plans with a large safe insertion surface.

Steerable needles that are able to follow curvilinear trajectories and steer around anatomical obstacles are a promising solution for many interventional procedures. In the lung, these needles can be deployed from the tip of a conventional bronchoscope to reach lung lesions for diagnosis. The reach of such a device depends on several design parameters including the bronchoscope diameter, the angle of the piercing device relative to the medial axis of the airway, and the needle's minimum radius of curvature while steering. Assessing the effect of these parameters on the overall system's clinical utility is important in informing future design choices and understanding the capabilities and limitations of the system. In this paper, we analyze the effect of various settings for these three robot parameters on the percentage of the lung that the robot can reach. We combine Monte Carlo random sampling of piercing configurations with a Rapidly-exploring Random Trees based steerable needle motion planner in simulated human lung environments to asymptotically accurately estimate the volume of sites in the lung reachable by the robot. We highlight the importance of each parameter on the overall system's reachable workspace in an effort to motivate future device innovation and highlight design trade-offs.

The inspection-planning problem calls for computing motions for a robot that allow it to inspect a set of points of interest (POIs) while considering plan quality (e.g., plan length). This problem has applications across many domains where robots can help with inspection, including infrastructure maintenance, construction, and surgery. Incremental Random Inspection-roadmap Search (IRIS) is an asymptotically-optimal inspection planner that was shown to compute higher-quality inspection plans orders of magnitudes faster than the prior state-of-the-art method. In this paper, we significantly accelerate the performance of IRIS to broaden its applicability to more challenging real-world applications. A key computational challenge that IRIS faces is effectively searching roadmaps for inspection plans—a procedure that dominates its running time. In this work, we show how to incorporate lazy edge-evaluation techniques into IRIS’s search algorithm and how to reuse search efforts when a roadmap undergoes local changes. These enhancements, which do not compromise IRIS’s asymptotic optimality, enable us to compute inspection plans much faster than the original IRIS. We apply IRIS with the enhancements to simulated bridge inspection and surgical inspection tasks and show that our new algorithm for some scenarios can compute similar-quality inspection plans 570× faster than prior work.

We present a method that plans motions for a concentric tube robot to automatically reach surgical targets inside the body while avoiding obstacles, where the patient's anatomy is represented by point clouds. Point clouds can be generated intra-operatively via endoscopic instruments, enabling the system to update obstacle representations over time as the patient anatomy changes during surgery. Our new motion planning method uses a combination of sampling-based motion planning methods and local optimization to efficiently handle point cloud data and quickly compute high quality plans. The local optimization step uses an interior point optimization method, ensuring that the computed plan is feasible and avoids obstacles at every iteration. This enables the motion planner to run in an anytime fashion, i.e., the method can be stopped at any time and the best solution found up until that point is returned. We demonstrate the method's efficacy in three anatomical scenarios, including two generated from endoscopic videos of real patient anatomy.

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.