optimizing MotionPlanning Problem Setup via Bounded Evaluation with Application to Following Surgical Trajectories A motionplanning problemxe2x80x99s setup can drastically affect the quality of solutions returned by the planner. In this work we consider optimizing these setups, with a focus on doing so in a computationallyefficient fashion. Our approach interleaves optimization with motion planning, which allows us to consider the actual motions required of the robot. Similar prior work has treated the planner as a black box: our key insight is that opening this box in a simpleyeteffective manner enables a more efficient approach, by allowing us to bound the work done by the planner to optimizerrelevant computations. Finally, we apply our approach to a surgicallyrelevant motionplanning task, where our experiments validate our approach by moreefficiently optimizing the fixed insertion pose of a surgical robot.

Planning HighQuality Motions for Concentric Tube Robots in Point Clouds via Parallel Sampling and optimization 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 patientxe2x80x99s anatomy is represented by point clouds. Point clouds can be generated intraoperatively 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 samplingbased 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 methodxe2x80x99s efficacy in three anatomical scenarios, including two generated from endoscopic videos of real patient anatomy.