Dynamic Multi-Heuristic A*

Many motion planning problems in robotics are high dimensional planning problems. While sampling-based motion planning algorithms handle the high dimensionality very well, the solution qualities are often hard to control due to the inherent randomization. In addition, they suffer severely when the configuration space has several ‘narrow passages’. Search-based planners on the other hand typically provide good solution qualities and are not affected by narrow passages. However, in the absence of a good heuristic or when there are deep local minima in the heuristic, they suffer from the curse of dimensionality. In this work, our primary contribution is a method for dynamically generating heuristics, in addition to the original heuristic(s) used, to guide the search out of local minima. With the ability to escape local minima easily, the effect of dimensionality becomes less pronounced. On the theoretical side, we provide guarantees on completeness and bounds on suboptimality of the solution found. We compare our proposed method with the recently published Multi-Heuristic A* search, and the popular RRT-Connect in a full-body mobile manipulation domain for the PR2 robot, and show its benefits over these approaches.

Authors

• Fahad Islam 0002
• Venkatraman Narayanan
• Maxim Likhachev

Keywords

• Robots
• Heuristic algorithms
• Search problems
• Planning
• Mobile communication
• Dynamics
• Euclidean distance
• High-dimensional
• Local Minima
• Path Planning
• Solution Quality
• Curse Of Dimensionality
• Configuration Space
• Planning Problem
• Theoretical Side
• Problem In Robotics
• Mobile Manipulator
• State Space
• State Value
• Heuristic Search
• Goal State
• Magnetic State
• Planning Time
• Average Path Length
• Starting State
• Successor States
• Robot State
• Motion Primitives
• Simple Heuristics
• Rejection Sampling
• Priority Queue
• Voter Preferences
• Dynamic Search
• Cost Path
• Local Minimum Problem