Partition Sampling Strategy for Robot Motion Planning in Narrow Passage Under Uncertainty

To address the perception and motion uncertainty issues for motion planning in narrow passage environments, a Partitioned Sampling Strategy based on Partially Observable Markov Decision Processes (POMDP) is put forward. Combining the partition sampling strategy with the POMDP algorithm improves the success rate of robot motion planning under narrow channel. Firstly, the division sampling strategy is adopted to divide the robot workspace into open area and narrow area, and connecting fewer sampling points to generate the initial trajectory of the robot; After the initial trajectory is generated, we further consider the uncertainty factors to make the path performance better. The POMDP model is used to solve the uncertainty problem; the local optimal solution is obtained by solving the POMDP problem, and the local optimal solution is iterated until the global optimal trajectory is obtained. The belief dynamics uses Extended Kalman Filter updating, and the belief space variables of iterative LQG are used for value iteration. The experimental results show that the appeal scheme can solve the motion planning problem of the robot in the narrow channel and the uncertain condition.