2016/07/21 by Alan Kuntz, Kuntz, Alan, Chris Bowen +3
Computer Science · Engineering · #FOS: Computer and information sciences #Robot Manipulation and Learning #Robotic Mechanisms and Dynamics #Robotic Path Planning Algorithms #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.1607.06374
openalex publication_date 2016/07/21 · openalex created_date 2016/08/23 · openalex updated_date 2026/07/28
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.