The.Algorithm.Design.Manual.Springer-Verlag.1998

Motion Planning
The most practical approach to motion planning involves randomly sampling the configuration space of
the robot. The configuration space defines the set of legal positions for the robot and has one dimension
for each degree of freedom. For a planar robot capable of translation and rotation, the degrees of freedom
are the x- and y-coordinates of a reference point on the robot and the angle relative to this point. Certain
points in this space represent legal positions, while others intersect obstacles.
Construct a set of legal configuration-space points by random sampling. For each pair of points and ,
decide whether there exists a direct, nonintersecting path between them. Construct a graph with vertices
for each legal point and edges for each such traversable pair. The problem of finding a motion between
two arbitrary positions reduces to seeing if there is a direct path from the initial/final position to some
vertex in the graph, and then solving a shortest-path problem in the graph.
There are lots of ways to enhance this basic technique for specific applications, such as adding additional
vertices to regions of particular interest. This is a nice, clean approach for solving problems that would
get very messy otherwise.
Implementations: An implementation of collision detection (not really motion planning) is the
I_COLLIDE collision detection library. For more information, check out the I_COLLIDE WWW page:
http://www.cs.unc.edu/ geom/I_COLLIDE.html.
O'Rourke [O'R94] gives a toy implementation of an algorithm to plot motion for a two-jointed robot arm
in the plane. See Section .
Notes: Motion planning was originally studied by Schwartz and Sharir as the ``piano mover's problem.''
Their solution constructs the complete free space of robot positions which do not intersect obstacles, and
then finds the shortest path within the proper connected component. These free space descriptions are
very complicated, involving arrangements of higher-degree algebraic surfaces. The fundamental papers
on the piano mover's problem appear in [HSS87], with [SS90] being a survey of current results. The best
general result for this free space approach to motion planning is due to Canny [Can87], who showed that
any problem with d degrees of freedom can be solved in , although faster algorithms exist for
special cases of the general motion planning problem.
Latombe's book [Lat91] describes practical approaches to motion planning, including the random
sampling method described above. The expanded obstacle approach to motion planning is due to Lozano-
Perez and Wesley [LPW79]. The heuristic, sightless man's approach to motion planning discussed above
has been studied by Lumelski [LS87].
The time complexity of algorithms based on the free-space approach to motion planning depends
intimately on the combinatorial complexity of the arrangement of surfaces defining the free space.
Algorithms for maintaining arrangements are presented in Section . Davenport-Schintzl sequences
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