Learning Action Models for Navigation in Noisy Environments

Abstract

To be e#ective, a navigation planner must have knowledge not only of the e#ects an action will have, but also the e#ects that the environment will have on that action (e.g. the robot may travel more slowly over rough terrain) . To address this issue, we have developed an approach called ERA which uses regression tree induction to learn action models that predict the e#ect terrain conditions will have on a robot's navigation actions. The action models support a high level planner that finds e#cient navigation plans. We present the results of a study which evaluated the performance of ERA in environments with noisy e#ectors and sensors. 1. Introduction An e#ective navigation planner for a mobile robot must be able to deal with large state spaces and take into account the uncertainty in the environment. One common approach to dealing with large state spaces is to use a high level planner that operates on a graph representing an abstract map of the area (Arkin, 1998). Each ...