Adaptation by applying behavior routines and motion strategies in autonomous navigation

Abstract

This paper presents our current efforts toward development of highlevel behavior routines and motion strategies for the stepwise case-based reasoning (SCBR) approach. The SCBR approach provides an appropriate architectural framework for autonomous navigation system in which situation cases are used to support the situation module, and route cases are used to support the high-level route planning module. In the SCBR approach, adaptation knowledge comes in the form of high-level behavior routines and motion strategies. The SCBR system determines next action based on an analysis of the generated view in terms of positions of relevant objects. Thus, higher-level case-based symbolic reasoning intervenes at the action selection points to determine which action vector is appropriate to control the SCBR system. In order to qualitatively evaluate the SCBR approach, we have developed a simulation environment. This simulation environment allows us to visually evaluate the progress of an SCBR system while it runs through a predefined virtual world.