An Adaptive Multi-resolution State Lattice Approach for Motion Planning with Uncertainty

In this paper we present a reliable motion planner that takes into account the kinematic restrictions, the shape of the robot and the motion uncertainty along the path. Our approach is based on a state lattice that predicts the uncertainty along the paths and obtains the one which minimizes both the probability of collision and the cost. The uncertainty model takes into account the stochasticity in motion and observations and the corrective effect of using a Linear Quadratic Gaussian controller. Moreover, we introduce an adaptive multi-resolution lattice that selects the most adequate resolution for each area of the map based on its complexity. Experimental results, for several environments and robot shapes, show the reliability of the planner and the effectiveness of the multi-resolution approach for decreasing the complexity of the search.