An industrial robot routinely carrying out an assembly or welding task
is an impressive sight. More important, when operated within its design
conditions it is a reliable production machine which - depending on the
manufacturing process being automated - is relatively quick to bring
into operation and can often repay its capital cost within a year or
two. Yet first impressions can be deceptive: if the workpieces deviate
somewhat in size or position, or, worse; if a gripper slips or a feeder
jams the whole system may halt and look very unimpressive indeed. This
is mainly because the sum total of the system's knowledge is simply a
list of a few variables describing a sequence of positions in space; the
means of moving from one to the next; how to react to a few input
signals; and how to give a few output commands to associated machines.
The acquisition, orderly retention and effective use of knowledge are
the crucial missing techniques whose inclusion over the coming years
will transform today's industrial robot into a truly robotic system
embodying the 'intelligent connection of perception to action'. The use
of computers to implement these techniques is the domain of Artificial
Intelligence (AI) (machine intelligence). Evidently, it is an essential
ingredient in the future development of robotics; yet the relationship
between AI practitioners and robotics engineers has been an uneasy one
ever since the two disciplines were born.