  Autonomous mobile robots (e.g., warehouse logistics robots) often need to
traverse complex, obstacle-rich, and changing environments to reach multiple
fixed goals (e.g., warehouse shelves). Traditional motion planners need to
calculate the entire multi-goal path from scratch in response to changes in the
environment, which result in a large consumption of computing resources. This
process is not only time-consuming but also may not meet real-time requirements
in application scenarios that require rapid response to environmental changes.
In this paper, we provide a novel Multi-Goal Motion Memory technique that
allows robots to use previous planning experiences to accelerate future
multi-goal planning in changing environments. Specifically, our technique
predicts collision-free and dynamically-feasible trajectories and distances
between goal pairs to guide the sampling process to build a roadmap, to inform
a Traveling Salesman Problem (TSP) solver to compute a tour, and to efficiently
produce motion plans. Experiments conducted with a vehicle and a snake-like
robot in obstacle-rich environments show that the proposed Motion Memory
technique can substantially accelerate planning speed by up to 90\%.
Furthermore, the solution quality is comparable to state-of-the-art algorithms
and even better in some environments.
