This thesis presents a reliable and efficient motion planning approach based on state lattices
for the autonomous navigation of mobile robots and UAVs. The proposal retrieves optimal
paths in terms of safety and traversal time, and deals with the kinematic constraints and the
motion and sensing uncertainty at planning time. The efficiency is improved by a novel
graduated fidelity state lattice which adapts to the obstacles in the map and the
maneuverability of the robot, and by a new multi-resolution heuristic which reduces the
computational complexity. The motion planner also includes a novel method to reliably
estimate the probability of collision of the paths considering the uncertainty in heading and
the robot dimensions