Improving Incremental Planning Performance through Overlapping Replanning and Execution

Deployment of motion planning algorithms in practical applications has lagged due to their slow speed in reacting to disturbances. We believe that the best way to address this is to reuse learned planning and control information across queries. In previous work, we introduced Chekov, a reactive, integrated motion planning and execution system that reuses learned information in the form of an enhanced roadmap. We have previously shown how we can use Chekov to formulate trajectory optimization problems that result in superior performance in static environments. In this work, we show how incremental planning can be incorporated into the formulation of optimized trajectories from roadmap seed trajectories. Further, we show how an incremental planner can be adapted to reduce the overhead incurred for replanning when trajectories become invalid during execution.