Paper Info
Title
Interleaving Optimization with Sampling-Based Motion Planning (IOS-MP): Combining Local Optimization with Global Exploration.
Abstract
Computing globally optimal motion plans for a robot is challenging in part because it requires analyzing a robotu0027s 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.
Year
Venue
Field
2016
arXiv: Robotics
Motion planning,Mathematical optimization,Simulation,Workspace,Curse of dimensionality,Sampling (statistics),Local search (optimization),Robot,Mathematics,Interleaving,Configuration space
DocType
Volume
Citations 
Journal
abs/1607.06374
0
PageRank 
References 
Authors
0.34
11
3
Name
Order
Citations
PageRank
Alan Kuntz134.79
Chris Bowen2101.66
Ron Alterovitz387359.61