Page 130 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

VISION AND MOTION PLANNING 105

needed in order to fully utilize additional sensing capabilities. We will consider
in detail two principles for provably correct motion planning with vision. As we
will see, the resulting algorithms exhibit different “styles” of behavior and are
not, in general, superior to each other. Third and very interestingly, while one
can expect great improvements in real-world tasks, in general richer sensing has
no effect on algorithm path length performance bounds.
Algorithms that we are about to consider will demonstrate an ability that is
often referred to in the literature as active vision [61, 62]. This ability goes deeply
into the nature of interaction between sensing and control. As experimentalists
well know, scanning the scene and making sense of acquired information is a
time-consuming operation. As a rule, the robot’s “eye” sees a bewildering amount
of details, almost all of which are irrelevant for the robot’s goal of finding its way
around. One needs a powerful mechanism that would reject what is irrelevant
and immediately use what is relevant so that one can continue the motion and
continue gathering more visual data. We humans, and of course all other species
in nature that use vision, have such mechanisms.
As one will see in this section, motion planning algorithms with vision that we
will develop will provide the robot with such mechanisms. As a rule, the robot
will not scan the whole scene; it will behave much as a human when walking
along the street, looking for relevant information and making decisions when the
right information is gathered. While the process is continuous, for the sake of
this discussion it helps to consider it as a quasi-discrete.
Consider a moment when the robot is about to pass some location. A moment
earlier, the robot was at some prior location. It knows the direction toward the
target location of its journey (or, sometimes, some intermediate target in the
visible part of the scene). The first thing it does is look in that direction, to see
if this brings new information about the scene that was not available at the prior
position. Perhaps it will look in the direction of its target location. If it sees an
obstacle in that direction, it may widen its “scan,” to see how it can pass around
this obstacle. There may be some point on the obstacle that the robot will decide
to head to, with the idea that more information may appear along the way and
the plan may be modified accordingly.
Similar to how any of us behaves when walking, it makes no sense for the
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robot to do a 360 scan at every step—or ever. Based on what the robot sees
ahead at any moment, it decides on the next step, executes it, and looks again for
more information. In other words, robot’s sensing dictates the next step motion,
and the next step dictates where to look for new relevant information.It is this
sensing-planning control loop that guides the robot’s active vision, and it is
executed continuously.
The first algorithm that we will consider, called VisBug-21, is a rather simple-
minded and conservative procedure. (The number “2” in its name refers to the
Bug2 algorithm that is used as its base, and “1” refers to the first vision algo-
rithm.) It uses range data to “cut corners” that would have been produced by
a “tactile” algorithm Bug2 operating in the same scene. The advantage of this
modification is clear. Envision the behavior of two people, one with sight and the