Path Planning for Surgery Robot with Bidirectional Continuous Tree Search and Neural Network Solving a thorny issue of realtime path planning for surgery robot in uncertain environments, a novel algorithm named bidirectional continuous tree search (BCTS) is proposed. Most partially observable markov decision process (POMDP) planners address challenges of unknown environments with discrete states, observations and actions, which are fail to automate the operative procedure. However, the BCTS method addresses the issue by handling POMDPs in continuous state, observation and action spaces. The proposed approach has a bidirectional search structure with the intent of greatly improving the calculation efficiency. Meanwhile, Bayesian optimization (BO) algorithm is considered to dynamically sample promising actions while we construct a belief tree. In view of the speed of BO process, the upper and lower bounds of the optimal action values given by fast informed bound (FIB) and pointbased value iteration (PBVI) limit the search scope, so we can improve the speed of BO. In addition, we apply an optimal path planning generator, radial basis function neural network (RBFNN), to obtain a smoother trajectory. Finally, simulation of glaucoma surgery has been carried out to explore the best surgical approach. The results show that the introduced structure can effectively guide the surgery robot to perform surgical procedures and receive a realtime as well as smooth path.

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.