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Computing globally optimal motion plans for a robot is challenging in partbecause it requires analyzing a robot's configuration space simultaneously fromboth a macroscopic viewpoint (i.e., considering paths in multiple homotopicclasses) and a microscopic viewpoint (i.e., locally optimizing path quality).We introduce Interleaved Optimization with Sampling-based Motion Planning(IOS-MP), a new method that effectively combines global exploration and localoptimization to quickly compute high quality motion plans. Our approachcombines two paradigms: (1) asymptotically-optimal sampling-based motionplanning, which is effective at global exploration but relatively slow atlocally refining paths, and (2) optimization-based motion planning, whichlocally optimizes paths quickly but lacks a global view of the configurationspace. IOS-MP iteratively alternates between global exploration and localoptimization, sharing information between the two, to improve motion planningefficiency. We evaluate IOS-MP as it scales with respect to dimensionality andcomplexity, as well as demonstrate its effectiveness on a 7-DOF manipulator fortasks specified using goal configurations and workspace goal regions.

Interleaving Optimization with Sampling-Based Motion Planning (IOS-MP): Combining Local Optimization with Global Exploration