Mobile robot navigation based on expected state value under uncertainty of self-localization

Uncertainty of self-localization is one of the most serious problems related to navigation of autonomous mobile robots. There are some studies that deal with this problem. However, most of them assume that the extent of the uncertainty is known or has been measured previously, though it changes easily with some trivial changes of the environment. To avoid this assumption, we take the following approach: 1) a robot uses a self- localization method that is quite robust against sensing noise and the environment change, 2) the plan for robot behavior is based on the assumption that the robot can recognize the environment perfectly, 3) a novel decision-making algorithm computes proper behavior from the planning result and self-localization results in real time. These algorithms were implemented on a soccer robot of the RoboCup four-legged robot league, and their efficiency was verified with experiments.