Page 32 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

INTRODUCTION 7

design. In comparison with this task, designing a robot taxi driver carries much
more uncertainty and hence more difficulty. Though the robot driver will have
electronic maps of the city, and frequent remote updates of the map will help
decrease the uncertainty due to construction sites or street accidents, there will
still be a tremendous amount of uncertainty caused by less than ideally care-
ful human car drivers, bicyclists, children running after balls, cats and dogs
and squirrels crossing the road, potholes, slippery road, and so on. These will
require millions of motion planning decisions done on the fly. Still, a great
many objects that surround the robot are man-made and well known and can be
preprocessed.
Not so with mountain climbing—this task seems to present the extreme in
unstructured environment. While the robot climber would know exactly where its
goal is, its every step is unlike the step before, and every spike driven in the wall may
be the last one—solely due to the lack of complete input information. A tremendous
amount of sensing and appropriate intelligence would be needed to compensate for
this uncertainty. While seemingly a world apart and certainly not as dangerous, the
job of a robot nurse would carry no less uncertainty. Similar examples can be easily
found for automating tasks in agriculture, undersea exploration, at a construction
site on Earth or on the moon, in a kindergarten, and so on. 1
In terms of Figure 1.1, this book can be seen as an attempt to push the envelope
of what is possible in robotics further to the right along the uncertainty line. We
will see, in particular, that the technology that we will consider allows the robot to
operate at the extreme right in Figure 1.1 in one specific sense—it makes a robot
safe to itself and to its environment under a very high level of uncertainty. Given
the importance of this feature and the fact that practically all robots today operate at
the line’s extreme left, this is no small progress. Much, but certainly not everything,
will also become possible for robot motion planning under uncertainty.
What kind of input information and what kind of reasoning do we humans use
to plan our motion? Is this an easy or is it a difficult skill to formalize and pass
along to robots? What is the role of sensing—seeing, touching, hearing—in this
process? There must be some role for it—we know, for instance, that when a
myopic person takes off his glasses, his movement becomes more tentative and
careful. What is the role of dynamics, of our mass and speed and accelerations
relative to the surrounding objects? Again, there must be some role for it—we
slow down and plan a round cornering when approaching a street corner. Are
we humans universally good in motion planning tasks, or are some tasks more
difficult for us than others? How is it for robots? For human–robot teams?
Understanding the issues behind those questions took time, and not everything
is clear today. For a long time, researchers thought that the difficulties with motion
planning are solely about good algorithms. After all, if any not-so-smart animal
can successfully move in the unstructured world, we got to be able to teach our
robots to do the same. True, we use our eyes and ears and skin to sense the

1 The last example brings in still another important dimension: The allowed uncertainty depends much
on what is at stake.