We review a neuroplanner architecture for use in constructing subcognitive controllers and new application that uses it. These controllers have wo important properties: (1) the ability to learn the topology of three continuous spaces: a steering space, a control space, and an observation space, and (2) the ability to integrate the three spaces so that initial and goal steering conditions can suggest a sequence of control states that lead the controlled system to the goal in the presence of obstacles. The result is a rudimentary planner or guidance system that can be used for such subcognitive tasks as robot manipulator control, head/eye coordination, and task sequencing. In this paper, we consider the second domain. The term neuroplanner is intended to convey the impression that the planner is implemented neurally and is more rudimentary than the conventional symbolic planners typical of artificial intelligence research.
Daryl H. Graf, Wilf R. LaLonde