Page 16 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

PREFACE xv

If the required motion is to be repeated over and over again and if all the
objects in the robot workspace can be described precisely—as they are, for
example, on the car assembly line or in an automatic painting booth—using
robots to automate the task presents no principal difficulties today. Designing
the required trajectories for the tool in the robot hand is a purely geometric
problem, fully solvable by computer. (Depending on the task specifics, it may
of course require an unrealistically large amount of computation time, but this
is another matter.) Once the car model changes next year, the new data are fed
into the computer, and the required motion is recalculated. This is an example
of a structured task, and it takes place in a structured environment.The word
“structured” is roughly equivalent to “well-organized,” “known precisely,” “man-
made.” Objects in a structured environment can be safely assumed fully known
in space and time.
As a rule, a structured environment is designed, carefully and often at great
cost, by highly qualified professionals. From the standpoint of motion planning,
the input information that the robot needs in order to generate the desired motion
is available before the motion starts. What is needed is appropriate algorithms
for transforming this information into proper motion trajectories. Today there are
plenty of such algorithms. This setup represents the Intelligence–Motion planning
paradigm.
This algorithmic paradigm was formulated right at the beginning of robotics
as a field of science and technology, around the mid-1960s. Today the Intelli-
gence–Motion paradigm boasts a large literature, appearing under such names
as motion planning with complete information,or model-based motion planning,
or the Piano Mover’s model. The symbolism behind the latter term is that when
movers set out to move a piano, they can first sit down and figure out the whole
sequence of moves and turns and raisings and lowerings, before they start the
actual motion. After all, the physical setting that encompasses this information is
right there before them. (Except, one might comment, “Who in this world would
ever do it this way?” More likely the movers just say, “Let’s do it!”, and they
discuss every move as they get to it—thereby losing an opportunity to contribute
to a great theory.)
On the theoretical level, the problem of motion planning with complete infor-
mation is more or less closed: remarkably complete and enlightening studies of
the problem have provided computational complexity bounds, motion planning
algorithms, and deep insights into the problem. Which is not to say that all prob-
lems in this area are solved. Most of today’s work in this area is devoted to
special cases and to struggling with computational issues in realistic settings.
Somewhat ironically, applications where such techniques are used today relate
not so much to robotics as to other areas: computer-aided design (CAD, e.g., to
design an aircraft engine such as to allow quick removal or replacement of a
given unit), models of protein folding in biology, and a few others. The major
property of such tasks is that the required motion is designed in a database rather
than in a physical setting. Given the wealth of published work in this area, this
book reviews the Piano Mover’s paradigm only cursorily.