Title: Improving Trajectory Optimization using a Roadmap Framework,
Abstract: We present an evaluation of several representative sampling-based and
optimization-based motion planners, and then introduce an integrated motion
planning system which incorporates recent advances in trajectory optimization
into a sparse roadmap framework. Through experiments in 4 common application
scenarios with 5000 test cases each, we show that optimization-based or
sampling-based planners alone are not effective for realistic problems where
fast planning times are required. To the best of our knowledge, this is the
first work that presents such a systematic and comprehensive evaluation of
state-of-the-art motion planners, which are based on a significant amount of
experiments. We then combine different stand-alone planners with trajectory
optimization. The results show that the combination of our sparse roadmap and
trajectory optimization provides superior performance over other standard
sampling-based planners combinations. By using a multi-query roadmap instead of
generating completely new trajectories for each planning problem, our approach
allows for extensions such as persistent control policy information associated
with a trajectory across planning problems. Also, the sub-optimality resulting
from the sparsity of roadmap, as well as the unexpected disturbances from the
environment, can both be overcome by the real-time trajectory optimization
process.