In this paper we present an overview of recent developments in the plan-based control of autonomous robots. We identify computational principles that enable autonomous robots to accomplish complex, diverse, and dynamically changing tasks in challenging environments. These principles include plan-based high-level control, probabilistic reasoning, plan transformation, and context and resource-adaptive reasoning. We will argue that the development of comprehensive and integrated computational models of plan-based control requires us to consider different aspects of plan-based control – plan representation, reasoning, execution, and learning – together and not in isolation. This integrated approach enables us to exploit synergies between the different aspects and thereby come up with simpler and more powerful computational models.
In the second part of the paper we describe
Structured Reactive Controllers(SRCs)
, our own approach to the development of a comprehensive computational model for the plan-based control of robotic agents. We show how the principles, described in the first part of the paper, are incorporated into the SRCs and summarize results of several long-term experiments that demonstrate the practicality of SRCs.