Page 204 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

INTRODUCTION 179

assurance that only the arm hand can be in danger of collisions is expensive and
can be justified only in a well-controlled environment, of which an automotive
factory floor is a good example. In general the practical use of such algorithms
is limited. They would not be of much use in tasks with a reasonable level of
uncertainty—as for example, outdoors.
As in the case of mobile robots, both exact (provable) and heuristic motion
planning algorithms have been explored for arm manipulators. It is important to note
that while good human intuition can sometimes justify the use of heuristic motion-
planningproceduresformobilerobots,nosuchintuitionexistsforarmmanipulators.
As we will see in Chapter 7, more often than not human intuition fails in motion
planning tasks for arm manipulators. One can read these results as a promise that a
heuristic automatic procedure will likely produce unpleasant surprises. Theoretical
assurances of algorithms’ convergence becomes a sheer necessity.
Similar to the situation with mobile robots (see Chapter 3), historically motion
planning for arm manipulators has received most attention in the context of the
paradigm with complete information (Piano Mover’s model). Both exact and
heuristic approaches have been explored [15, 16, 18, 20–22, 24, 25, 102]. Little
work has been done on motion planning with uncertainty [54].
In this and the next chapters, sensor-based motion planning will be applied to
the whole robot body. No point of the robot body should be in danger of a collision.
But bodies of robot arm manipulators are very complex. Parts move relative to
each other, and shapes are elaborate; it would not be feasible in practice to supply
a collision avoidance algorithm with an exact description of the robot body. Our
objective will be to make the algorithms immune to specifics of arm geometry.
Similar to how we approached the problem in Chapter 3, we will first consider
simple systems, namely, planar arm manipulators. These results may already
have some limited use in applications; for example, in terms of programming
and motion planning, a class of arm manipulators called SCARA (which stands
for Selective Compliant Articulated Robot Arm) consists of essentially plane-
oriented devices; they are used widely in tasks where the “main action” takes
place in a plane (such as assembly on a conveyer belt), and the third dimension
plays a secondary role. However, the main motivation behind the simpler cases
considered in this chapter is to develop a theoretical framework that will be used
in the next chapter to develop motion planning strategies for three-dimensional
(3D) robot arms of various kinematics.
The same as with mobile robots, the uncertainty of the robot surroundings
precludes a sensor-based algorithm from promising an optimal path for an arm
manipulator. Instead, the objective is to generate a “reasonable” path for the
arm (if one exists), or to conclude that the target position cannot be reached if
that happen to be so. We will discover that for the arm manipulators considered
here a purely local sensory feedback is sufficient to guarantee reaching a global
objective—that is, to guarantee algorithm convergence.
We will do the necessary analysis using the simplest tactile sensing and sim-
plified shapes for the robot. Since such simplifications often cause confusion as
to algorithms’ applicability, it is worthwhile to repeat these points: