<title>Controlling reactive behavior with consistent world modeling and reasoning</title>

摘要

Based on the philosophical view of reflexive behaviors and cognitive modules working in a complementary fashion, this paper proposes a hybrid decomposition of the control architecture for an intelligent, fully autonomous mobile robot. This architecture follows a parallel distributed decomposition and supports a hierarchy of control with lower-level reflexive type behaviors working in parallel with higher-level planning and map building modules. The behavior-based component of the system provides the basic instinctive competences for the robot while the cognitive part performs higher machine intelligence functions such as planning. The interface between the two components utilizes motivated behaviors implemented as part of the behavior-based system. A motivated behavior is one whose response is dictated mainly by the internal state (or the motivation state) of the robot. Thus, the cognitive planning activity can execute its plans by merely setting the motivation state of the robot and letting the behavior-based subsystem worry about the details of plan execution. The goal of such a hybrid architecture is to gain the real-time performance of a behavior-based system without losing the effectiveness of a general purpose world model and planner. We view world models as essential to intelligent interaction with the environment, providing a `bigger picture' for the robot when reactive behaviors encounter difficulty. We describe a live experimental run of our robot under hybrid control in an unknown and unstructured lab environment. This experiment demonstrated the validity of the proposed hybrid control architecture and the sensory knowledge integrator (the underlying model for the map-builder module) for the task of mapping the environment. Results of the emergent robot behavior and different map representations of the environment are presented and discussed.