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We present an evaluation of several representative sampling-based andoptimization-based motion planners, and then introduce an integrated motionplanning system which incorporates recent advances in trajectory optimizationinto a sparse roadmap framework. Through experiments in 4 common applicationscenarios with 5000 test cases each, we show that optimization-based orsampling-based planners alone are not effective for realistic problems wherefast planning times are required. To the best of our knowledge, this is thefirst work that presents such a systematic and comprehensive evaluation ofstate-of-the-art motion planners, which are based on a significant amount ofexperiments. We then combine different stand-alone planners with trajectoryoptimization. The results show that the combination of our sparse roadmap andtrajectory optimization provides superior performance over other standardsampling-based planners combinations. By using a multi-query roadmap instead ofgenerating completely new trajectories for each planning problem, our approachallows for extensions such as persistent control policy information associatedwith a trajectory across planning problems. Also, the sub-optimality resultingfrom the sparsity of roadmap, as well as the unexpected disturbances from theenvironment, can both be overcome by the real-time trajectory optimizationprocess.

Improving Trajectory Optimization using a Roadmap Framework