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Scalable Multicore Motion Planning Using Lock-Free Concurrency.

Jeffrey Ichnowski1, Ron Alterovitz1

  • 1Department of Computer Science at the University of North Carolina at Chapel Hill, USA.

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PubMed
Summary
This summary is machine-generated.

Parallel RRT (PRRT) and Parallel RRT* (PRRT*) offer efficient motion planning on multicore CPUs. These methods use lock-free techniques and parallel sampling for improved speed and scalability in complex robotics tasks.

Keywords:
concurrent algorithmsmotion and path planningsampling-based methods

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Area of Science:

  • Robotics
  • Computer Science
  • Artificial Intelligence

Background:

  • Motion planning is crucial for robot navigation and task execution.
  • Existing algorithms often struggle with scalability on modern multi-core processors.
  • Traditional parallelization methods can introduce performance bottlenecks like lock contention.

Purpose of the Study:

  • To develop novel parallel algorithms for motion planning.
  • To enhance the efficiency and scalability of RRT and RRT* on multi-core CPUs.
  • To introduce lock-free concurrency and optimized parallel strategies for motion planning.

Main Methods:

  • Parallel RRT (PRRT) and Parallel RRT* (PRRT*) algorithms were developed.
  • Algorithms utilize lock-free concurrency with atomic operations for shared data structures.
  • Techniques include partition-based sampling and parallel work-saving for improved cache efficiency and reduced rewiring.

Main Results:

  • PRRT and PRRT* demonstrate significant scalability with increasing core counts.
  • Superlinear speedups were observed in certain scenarios.
  • Successful application in complex tasks like the Alpha Puzzle, Cubicles, and a Nao robot manipulation task.

Conclusions:

  • PRRT and PRRT* provide efficient and scalable solutions for motion planning on multi-core CPUs.
  • The lock-free and optimized parallel approaches overcome limitations of traditional methods.
  • These CPU-based methods are readily integrable with existing robotics libraries.