Probabilistic Roadmap Methods (PRMs) solve the
motion planing problem by constructing a roadmap (or graph)
that models the motion space when feasible local motions exist.
PRMs and variants contain several phases during roadmap
generation i.e., sampling, connection, and query. Some work
has been done to apply machine learning to the connection
phase to decide which variant to employ, but it uses a global
learning approach that is inefficient in heterogeneous situations.
We present an algorithm that instead uses local learning: it
only considers the performance history in the vicinity of the
current connection attempt and uses this information to select
good candidates for connection. It thus removes any need to
explicitly partition the environment which is burdensome and
typically difficult to do. Our results show that our method learns
and adapts in heterogeneous environments, including a KUKA
youBot with a fixed and mobile base. It finds solution paths
faster for single and multi-query scenarios and builds roadmaps
with better coverage and connectivity given a fixed amount of
time in a wide variety of input problems. In all cases, our
method outperforms the previous adaptive connection method
and is comparable or better than the best individual method.