Robot Planning in the Space of Feasible Actions: Two Examples

Abstract

Several researchers in robotics and artificial intelligence have found that the commonly used method of planning in a state (configuration) space is intractable in certain domains. This may be because the C-space has very high dimensionality, the "C-space obstacles" are too difficult to compute, or because a mapping between desired states and actions is not straightforward. Instead of using an inverse model that relates a desired state to an action to be executed by a robot, we have used a methodology that selects between the feasible actions that a robot might execute, in effect, circumventing many of the problems faced by configuration space planners. In this paper we discuss the implications of such a method and present two examples of working systems that employ this methodology. One system drives an autonomous cross-country vehicle while the other controls a robotic excavator performing a trenching operation.