Page 359 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

334 HUMAN PERFORMANCE IN MOTION PLANNING

likely be something akin to the three examples shown in Figure 7.2. All examples
demonstrate exemplary performance; in fact, these paths are close to the opti-
2
mal—that is, the shortest—path from S to T . In the terminology used in this
text, what you have done here and what the paths in Figure 7.2 demonstrate is
motion planning with complete information.
If one tried to program a computer to do the same job, one would first prepro-
cess the labyrinth to describe it formally—perhaps segment the labyrinth walls
into small pieces, approximate those pieces by straight lines and polynomials for
a more efficient description, and so on, and eventually feed this description into
a special database. Then this information could be processed, for example, with
one or another motion planning algorithm that deals with complete information
about the task.
The level of detail and the respectable amount of information that this database
would encompass suggests that this method differs significantly from the one you
just used. It is safe to propose that you have paid no attention to small details
when attempting your solution, and did not try to take into account the exact
shapes and dimensions of every nook and cranny. More likely you concentrated
on some general properties of the labyrinth, such as openings in walls and where
those openings led and whether an opening led to a dead end. In other words,
you limited your attention to the wall connectivity and ignored exact geometry,
thus dramatically simplifying the problem. Someone observing you—call this
person the tester—would likely conclude that you possess some powerful motion
planning algorithm that you applied quickly and with no hesitation. Since it is
very likely that you never had a crash course on labyrinth traversal, the source
and nature of your powerful algorithm would present an interesting puzzle for
the tester.
Today we have no motion planning algorithms that, given complete informa-
tion about the scene, will know from the start which information can be safely
ignored and that will solve the task with the effectiveness you have demonstrated
a minute ago. The existing planning algorithms with complete information will
grind through the whole database and come up with the solution, which is likely
to be almost identical to the one you have produced using much less information
about the scene. The common dogma that humans are smarter than computers is
self-evident in this example.

Moving with Incomplete Information. What about a more realistic labyrinth
walk, where at any given moment the walker can see only the surrounding walls?
To test this case, let us use the same labyrinth that we used above (Figure 7.1),
except that we modify the user interface to reflect the new situation. As before,
you are sitting in front of the computer screen. You see on it only points S and
T and your own position in the labyrinth (the cursor). The whole labyrinth is
there, but it is invisible. As before, you start at S, moving the cursor with the
2 Of course, in a more complex labyrinth a quick look may not be sufficient to see the solution; then
one’s performance may deteriorate. For the point that we are to make in this section, this fact is not
essential.