Combining Navigational Planning and Reactive Control

Abstract

Traditional AI methods for navigational planning use qualitative spatial representations and reasoning. Tra-ditional robotics techniques for this task are based on numerical representations and reasoning. Recent work on robotics posits mechanisms for reactive control that directly map perceptions of the world to actions on it. This in turn has given rise to hybrid robot ar-chitectures that combine navigational planning and reactive control. But following traditional robotics techniques, navigational planning in these hybrid ar-clfitectures to() uses numerical methods. This raises the following question: Given a hybrid robot architec-ture, are numerical methods really needed for naviga-tional planning? To explore this issue, we integrated a multistrategy qualitative navigational planner with a reactive-control mechanism. Then we embodied the integrated system on a physical robot. Next we gave the robot a series of navigation tasks in a visually structured spatial world containing discrete pathways, and monitored its actions as it executed the tasks in the presence of both static and moving obstacles. Our experiments show that for hybrid robots qualitative methods are sufficient for navigational planning in at least one class of spatial worlds. Background, Motivations and Goals Spatial navigation is a classical problem in AI and robotics. ’~aditional AI methods for spatial naviga-tion rely on deliberative planning based on qualitative spatial representations and reasoning [Davis 1986]. For example, STRIPS [Fikes and Nilsson 1971; Fikes, Hart and Nilsson 1972] combined the methods of means-ends analysis and theorem proving to form qualitative plans. Its spatial representations captured topologi-cal relationships between spatial regions (e.g., rooms) *This work has benefited from many discussions with members of the AI and Robotics groups at Georgia In-stitute of Technology. We are especially gratefid to Ron Arkin for allowing us to play with AuRA and Stimpy. This research has been partially supported by a CER in-