On the role of stored internal state in the control of autonomous mobile robots

Abstract

This article informally examines the role of stored internal state (that is, memory) in the control of autonomous mobile robots. The difficulties associated with using stored internal state are reviewed. It is argued that the underlying cause of these problems is the implicit predictions contained within the state, and, therefore, many of the problems can be solved by taking care that the internal state contains information only about predictable aspects of the environment. One way of accomplishing this is to maintain internal state only at a high level of abstraction. The resulting information can be used to guide the actions of a robot but should not be used to control these actions directly; local sensor information is still necessary for immediate control. A mechanism to detect and recover from failures is also required. A control architecture embodying these design principles is briefly described. This architecture was successfully used to control real-world and simulated real-world autonomous mobile robots performing complex navigation tasks. The architecture is able to incorporate standard AI planning and world-modeling algorithms into a real-time situated framework.