Proprioceptive motion modeling for Monte Carlo localization

Abstract

This paper explores how robot localization can be improved and made more reactive by using an adaptive motion model based on proprioception. The motion model of mobile robots is commonly assumed to be constant or a function of the robot speed. We extend this model by explicitly modeling possible states of locomotion caused by interactions of the robot with its environment, such as collisions. The motion model thus behaves according to which state the robot is in. State transitions are based on proprioception, which in our case describes how well the robot's limbs are able to follow their respective motor commands. The extended, adaptive motion model yields a better, more reactive model of the current robot belief, which is shown in experiments. The improvement is due to the fact that the motion noise no longer has to subsume any possible outcome of actions including failure. In contrast, a clear distinction between failure and normal, desired operation is possible, which is reflected in the motion model. © Springer-Verlag Berlin Heidelberg 2007.