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Cache-Aware Asymptotically-Optimal Sampling-Based Motion Planning.

Jeffrey Ichnowski1, Jan F Prins1, Ron Alterovitz1

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Summary
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We introduce Cache-Aware Rapidly Exploring Random Tree* (CARRT*), a motion planner that speeds up computation by using CPU cache memory. This approach optimizes robot pathfinding by keeping data in the cache for faster searches.

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Area of Science:

  • Robotics
  • Computer Science
  • Artificial Intelligence

Background:

  • Motion planning is crucial for robot navigation.
  • Traditional methods can be computationally expensive.
  • Modern CPUs have complex cache hierarchies that impact performance.

Purpose of the Study:

  • To develop a motion planner that leverages CPU cache hierarchy.
  • To reduce computation time for sampling-based motion planning.
  • To improve the efficiency of nearest neighbor searches in configuration space.

Main Methods:

  • Introducing CARRT* (Cache-Aware Rapidly Exploring Random Tree*).
  • Implementing a cache-aware subdivision strategy for configuration space.
  • Utilizing a graph rewiring technique to complement subdivision.
  • Testing on point robot and Baxter robot scenarios.

Main Results:

  • CARRT* significantly reduces motion planning computation time.
  • Cache utilization accelerates nearest neighbor searching.
  • The approach demonstrates performance benefits in practical scenarios.
  • Optimality of the motion planning graph is refined more quickly.

Conclusions:

  • Cache-aware motion planning offers substantial computational advantages.
  • CARRT* effectively utilizes modern CPU cache memory for efficiency.
  • The proposed method enhances the performance of sampling-based motion planners.