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Parallel Sensor-Space Lattice Planner for Real-Time Obstacle Avoidance.

Bernardo Martinez Rocamora1, Guilherme A S Pereira1

  • 1Department of Mechanical and Aerospace Engineering, Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV 26506, USA.

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Summary
This summary is machine-generated.

This study introduces a fast parallel motion planner for robots and autonomous vehicles. The sensor-space lattice (SSLAT) algorithm enables real-time obstacle avoidance by rapidly computing collision-free paths.

Keywords:
obstacle avoidanceparallel computingpath planningrobotics

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Area of Science:

  • Robotics
  • Autonomous Systems
  • Computer Science

Background:

  • Real-time motion planning is crucial for autonomous vehicles and mobile robots.
  • Existing methods often struggle with dynamic environments and computational speed.

Purpose of the Study:

  • To present a novel parallel motion planner, the sensor-space lattice (SSLAT) algorithm.
  • To enable rapid, real-time collision-free path computation for mobile robots and autonomous vehicles.

Main Methods:

  • Utilizes lattices in the sensor space of planar range finders to tessellate the environment.
  • Employs a cost function to optimize paths guided by a vector field.
  • Implements both sequential and parallel versions for performance comparison.

Main Results:

  • The SSLAT motion planner computes paths in milliseconds, facilitating real-time obstacle avoidance.
  • Demonstrated faster collision checking and path planning compared to baseline methods in simulations and real experiments.
  • The parallel implementation achieved a speedup greater than 25, independent of obstacle complexity.

Conclusions:

  • The SSLAT algorithm offers a highly efficient solution for real-time motion planning in complex environments.
  • Parallelization significantly enhances performance, making it suitable for demanding autonomous applications.
  • The method effectively handles both static and dynamic obstacles in cluttered settings.