  Computing globally optimal motion plans for a robot is challenging in part
because it requires analyzing a robot's configuration space simultaneously from
both a macroscopic viewpoint (i.e., considering paths in multiple homotopic
classes) 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 local
optimization to quickly compute high quality motion plans. Our approach
combines two paradigms: (1) asymptotically-optimal sampling-based motion
planning, which is effective at global exploration but relatively slow at
locally refining paths, and (2) optimization-based motion planning, which
locally optimizes paths quickly but lacks a global view of the configuration
space. IOS-MP iteratively alternates between global exploration and local
optimization, sharing information between the two, to improve motion planning
efficiency. We evaluate IOS-MP as it scales with respect to dimensionality and
complexity, as well as demonstrate its effectiveness on a 7-DOF manipulator for
tasks specified using goal configurations and workspace goal regions.
