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

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 highly 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 demonstrate the effectiveness of IOS-MP in scenarios of varying complexity and dimensionality.