Page 29 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

4 MOTION PLANNING—INTRODUCTION

elderly individuals. So, why don’t we have it? What is missing? The answer is, yes,
something is missing, but often it is not sophistication and not functional abilities.
What is missing are two skills. One absolutely mandatory, is a local nature
and is a seemingly trivial “secondary” ability in a machine not to bump into
unexpected objects while performing its main task—be it walking toward a
person in a room with people and furniture, helping someone to dress, replacing
a book on the shelf, or “scuba-diving” in an undersea cave. Without this ability
the robot is dangerous to the environment and the environment is dangerous to
the robot—which for an engineer simply means that the robot cannot perform
tasks that require this ability. We can call this ability collision avoidance in an
uncertain environment.
The other skill, which we can call motion planning, or navigation, is of a global
nature and refers to the robot ability to guarantee arrival at the destination. The
importance of this skill may vary depending on a number of circumstances.
For humans and animals, passing successfully around a chair or a rock does
not depend on whether the chair or the rock is in a position that we “agreed”
upon before we started. The same should be true for a robot—but it is not.
Let us call the space in which the robot operates the robot workspace,or the
robot environment. If all objects present in the robot workspace could be described
precisely, to the smallest detail, automating the necessary motion would present
no principal difficulties. We would then be in the realm of what we call the
paradigm of motion planning with complete information. Though, depending on
details, the problem may require an inordinate computation time, this is a purely
geometric problem, and the relevant software tools are already there. Algorithmic
solutions for this problem started appearing in the late 1970s and were perfected
in the following decades.
A right application for such a strategy is, for example, one where the motion
has to be repeated over and over again in exactly the same workspace, precisely as
it happens on the car assembly line or in a car body painting booth. Here complete
information about all objects in the robot environment is collected beforehand
and passed to the motion planning software. The computed motion is then tried
and optimized via special software or/and via many trial-and-error improvements,
and only then used. Operators daily make sure that nothing on the line changes;
if it does purposely, the machine’s software is updated accordingly. Advantages
of this strategy are obvious: It delivers high accuracy and repeatability, consistent
quality, with no coffee breaks. If the product changes, say, in the next model
year, a similar “retraining” procedure is applied.
We will call tasks and environments where this approach is feasible structured
tasks and structured environments, which signifies the fact that objects in the
robot environment are fully known and predictable in space and time. Such
environments are, as a rule, man-made.
An automotive assembly line is a perfect example of a structured environment:
Its work cells are designed with great care, and usually at a great cost, so as to
respect the design constraints of robots and other machinery. A robot in such
a line always “knows” beforehand what to expect and when. Today the use of