Page 17 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

xvi PREFACE

The focus of this book is on unstructured tasks—tasks that unfold in an
unstructured environment, an environment that is not predesigned and has to be
taken as is. Most of the motion planning examples above (homes, outdoors, deep
space, etc.) refer to unstructured tasks. Until recently, robotics practitioners have
either ignored this area or have limited their efforts to grossly simplified tasks with
robot hands or with mobile robots. Even in the latter cases the operation is mostly
limited to a tight human teleoperation, with a minimum of robot autonomy (as in
the case of recent Mars rovers). All kinds of helpful “artificial” measures—for
example, an extremely slow operation—are taken to allow the operator to precede
commands with a careful analysis.
Automating motion planning for mobile robots will be considered in the first
sections of this text. We will also see later that teaching a robot arm manipulator
to safely move in an unstructured environment is a much taller order than the
same request for a mobile robot. This is unfortunate because a large number of
pressing applications require manipulators. Today people use a great deal more
arm manipulators than mobile robot vehicles. An arm manipulator is a device
similar to a human arm. If the task is to just move around and sense data or
take pictures, that is a job for a mobile robot. But if the task requires “doing
things”—welding, painting, putting things together or taking them apart—one
needs an arm manipulator. Interestingly, while collision avoidance is a major
bottleneck in the use of robot manipulators, there is minuscule literature on the
subject. This book attempts to fill the gap.
Objects in an unstructured robot workspace cannot be described fully—either
because of their unyielding shape, or because of lack of knowledge about them,
or because one doesn’t know which object is going to be where and when, or
because of all three. In dealing with an environment that has to be taken as is,
our robots have a good example to follow: The evolution has taught us humans
how to move around in our messy unstructured world. We want our robots to
leap-frog this process.
And then there are tasks—especially, as we will see, with motion planning
for arm manipulators—where human skills and intuition are not as enviable. In
fact, not enviable at all. Then not only do we need to enter unchartered terri-
tories and synthesize new robot motion planning strategies that are way beyond
human spatial reasoning skills, but also we must built a solid theoretical founda-
tion behind them, because human experience and heuristics cannot help ascertain
their validity.
If the input information about one’s surroundings is not available beforehand,
one cannot of course calculate the whole motion at once, or even in large pieces.
What do we humans and animals do in such cases? We compensate by real-time
sensing and sensor data processing: We look, touch, listen, smell, and continu-
ously use the sensing information to plan, execute, and replan our motion. Even
when one thinks one knows by heart how to move from point A to point B—say,
to drive from home to one’s office—the actual execution still involves a large
amount of continuous sensor-based motion planning.