Page 159 - Sensing, Intelligence, Motion : How Robots and Humans Move in an Unstructured World

134 MOTION PLANNING FOR A MOBILE ROBOT

the above difficulty with the algorithm convergence in the situation with moving
obstacles. 10 (More details on this model can be found in Ref. 77.)

Needs for More Complex Algorithms. One area where good analysis of algo-
rithms is extremely important for theory and practice is sensor-based motion
planning for robot arm manipulators. Robot manipulators operate sometimes in
a two-dimensional space, but more often they operate in the three-dimensional
space. They have complex kinematics, and they have parts that change their rel-
ative positions in complex ways during the motion. Not rarely, their workspace
is filled with obstacles and with other machinery (which is also obstacles).
Careful motion planning is essential. Unlike with mobile robots, which usually
have simple shapes and can be controlled in an intuitively clear fashion, intuition
helps little in designing new algorithms or even predicting the behavior of existing
algorithms for robot arm manipulators.
As mentioned above, performance of Bug2 algorithm deteriorates when deal-
ing with situations that we called in-position. In fact, this will be likely so for all
Class 2 motion planning algorithms. Paths tend to become longer, and the robot
may produce local cycles that keep “circling” in some segments of the path.
The chance of in-position situations becomes very persistent, almost guaranteed,
with arm manipulators. This puts a premium on good planning algorithms. This
area is very interesting and very unintuitive. Recall that today about 1,000,000
industrial arms manipulators are busy fueling the world economy. Two chapters
of this book, Chapters 5 and 6, are devoted to the topic of sensor-based motion
planning for arm manipulators.
The importance of motion planning algorithms for robot arm manipulators is
also reinforced by its connection to teleoperation systems. Space-operator-guided
robots (such as arm manipulators on the Space Shuttle and International Space
Station), robot systems for cleaning nuclear reactors, robot systems for detonating
mines, and robot systems for helping in safety operations are all examples of
teleoperation systems. Human operators are known to make mistakes in such
tasks. They have difficulty learning necessary skills, and they tend to compensate
difficulties by slowing the operation down to crawling. (Some such problems will
be discussed in Chapter 7.) This rules out tasks where at least a “normal” human
speed is a necessity.
One potential way out of this difficulty is to divide responsibilities between
the operator and the robot’s own intelligence, whereby the operator is responsible
for higher-level tasks—planning the overall task, changing the plan on the fly
if needed, or calling the task off if needed—whereas the lower-level tasks like
obstacle collision avoidance would be the robot’s responsibility. The two types
of intelligence, human and robot intelligence, would then be combined in one
control system in a synergistic manner. Designing the robot’s part of the system
would require (a) the type of algorithms that will be considered in Chapters 5
and 6 and (b) sensing hardware of the kind that we will explore in Chapter 8.

10 Note that this is the spirit of the automobile traffic rules.