  Sampling-based motion planning algorithms have been continuously developed
for more than two decades. Apart from mobile robots, they are also widely used
in manipulator motion planning. Hence, these methods play a key role in
collaborative and shared workspaces. Despite numerous improvements, their
performance can highly vary depending on the chosen parameter setting. The
optimal parameters depend on numerous factors such as the start state, the goal
state and the complexity of the environment. Practitioners usually choose these
values using their experience and tedious trial and error experiments. To
address this problem, recent works combine hyperparameter optimization methods
with motion planning. They show that tuning the planner's parameters can lead
to shorter planning times and lower costs. It is not clear, however, how well
such approaches generalize to a diverse set of planning problems that include
narrow passages as well as barely cluttered environments. In this work, we
analyze optimized planner settings for a large set of diverse planning
problems. We then provide insights into the connection between the
characteristics of the planning problem and the optimal parameters. As a
result, we provide a list of recommended parameters for various use-cases. Our
experiments are based on a novel motion planning benchmark for manipulators
which we provide at https://mytuc.org/rybj.
