Page 244 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

PLANAR REVOLUTE–REVOLUTE (RR) ARM 219

Algorithm, not lastly because this C-space structure allows for more than
one “short” route between the start and target positions, which have been
used with profit by the algorithm. The analysis demonstrates that the arm
kinematics can greatly influence the algorithm structure. In the following
sections we will study in a similar vein the remaining four of the five con-
figurations of planar two-link arms shown in Figure 5.1. We will conclude
these studies with an attempt, in Section 5.8.4, to develop a unifying theory
that will allow one to consider each of the five kinematics of Figure 5.1 as
a special instant of one general case.
• Planning of arm motion with the described RR-Arm Algorithm is done
completely in the workspace (W-space), based on the sensing data from
the arm sensors. The above analysis and examples referring to the arm
configuration space have been used solely to establish the theory and develop
the algorithmic machinery.
• A similar consideration applies to the sensing used. Whereas most of our
algorithm design process relied on tactile sensing, this was done only for
the sake of simpler explanation. As discussed in Section 5.2.5 (and more
in Chapter 8), proximity sensing, and not tactile sensing, should be used in
practical arm manipulator motion planning systems.
• No preliminary exploration of obstacles and no beforehand partial or com-
plete computation of the scene in W-space or C-space takes place or is
expected by the algorithm. By the time the arm arrives at the target location,
it may know very little about the space that it just traversed.
• If the desired target position is not feasible because of interfering obsta-
cles, the reachability test built into the algorithm will make this conclusion,
usually quickly enough and without exploring the whole space.
• The algorithm plans the robot arm motion better than humans do. We will
discuss this interesting observation in great detail in Chapter 7. In brief,
when watching the RR-Arm Algorithm in action, humans have difficulty
grasping its mechanism or the rationale behind the paths it generates. A
quick glance at the paths in Figures 5.14, 5.15, and 5.18 should help con-
vince one that this is so. This is so even for simple scenes, and it is so for
tactile as well as for more complex sensing. The difficulty for humans is not
in that the algorithm is overly complex. With quick training, one will be able
to understand and use the RR-Arm Algorithm in C-space—but not in W-
space. Unfortunately, this would be a useless demonstration because C-space
is not available for motion planning; remember, our primary assumption is
that no information about the scene is available beforehand. On the other
hand, asking human operators to use the algorithm in the workspace, the
way a robot arm manipulator does it, turns out to be hopeless. And humans
own strategies, whatever they are, consistently show an inferior performance
compared to that of RR-Arm Algorithm (see Chapter 7).

Recall how very different our current situation is from the one we faced with
mobile robot motion planning algorithms (Chapter 3). We observed there that,