  When facing a new motion-planning problem, most motion planners solve it from
scratch, e.g., via sampling and exploration or starting optimization from a
straight-line path. However, most motion planners have to experience a variety
of planning problems throughout their lifetimes, which are yet to be leveraged
for future planning. In this paper, we present a simple but efficient method
called Motion Memory, which allows different motion planners to accelerate
future planning using past experiences. Treating existing motion planners as
either a closed or open box, we present a variety of ways that Motion Memory
can contribute to reduce the planning time when facing a new planning problem.
We provide extensive experiment results with three different motion planners on
three classes of planning problems with over 30,000 problem instances and show
that planning speed can be significantly reduced by up to 89% with the proposed
Motion Memory technique and with increasing past planning experiences.
