So far there has been no explicit consideration of the topology of
. Assuming that
is a manifold, the concepts discussed so far
can be applied to any open set on which coordinates are defined. In
practice, it is often convenient to use the manifold representations
of Section 4.1.2. The manifold can be expressed as a
cube,
, with some faces identified to obtain
.
Over the interior of the cube, all of the concepts explained in this
section work without modification. At the boundary, the samples used
for interpolation must take the identification into account.
Furthermore, actions,
, and next states,
, must function
correctly on the boundary. One must be careful, however, in declaring
that some solution is optimal, because Euclidean shortest paths depend
on the manifold parameterization. This ambiguity is usually resolved
by formulating the cost in terms of some physical quantity, such as
time or energy. This often requires modeling dynamics, which will be
covered in Part IV.
Value iteration with interpolation is extremely general. It is a
generic algorithm for approximating the solution to optimal control
problems. It can be applied to solve many of the problems in Part
IV by restricting to take into account complicated
differential constraints. The method can also be extended to problems
that involve explicit uncertainty in predictability. This version of
value iteration is covered in Section 10.6.
Steven M LaValle 2020-08-14