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End-to-End One-Shot Path-Planning Algorithm for an Autonomous Vehicle Based on a Convolutional Neural Network

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Summary
This summary is machine-generated.

This study introduces a fast, end-to-end path-planning algorithm using a fully convolutional neural network (FCNN) for robotics and automated driving. The novel FCNN model efficiently finds optimal paths on various grid maps, outperforming traditional methods in speed and success rate.

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deep learningfully convolutional neural networkgrid mappath planning

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Computer Vision

Background:

  • Path planning is crucial for robot navigation and automated driving.
  • Existing iterative path-planning methods can be slow, especially for large or complex environments.
  • There is a need for faster and more efficient path-planning solutions.

Purpose of the Study:

  • To develop an end-to-end path-planning algorithm using a fully convolutional neural network (FCNN).
  • To enable efficient path planning on grid maps of varying sizes and shapes (10x10 to 80x80).
  • To provide a model capable of finding both the lowest-cost and shortest paths.

Main Methods:

  • Implemented an end-to-end path-planning algorithm based on a fully convolutional neural network (FCNN).
  • Trained a general path-planning model considering traversability cost for grid maps.
  • The FCNN model generates probability maps for lowest-cost and shortest paths, reconstructing the optimal path via highest probability selection.

Main Results:

  • The proposed FCNN method demonstrates superior speed advantages over traditional algorithms.
  • Achieved average optimal rates of 72.7% for lowest-cost paths and 78.2% for shortest paths.
  • Reported average success rates of 95.1% and 92.5%, with average length rates of 1.04 and 1.03, respectively.

Conclusions:

  • The end-to-end FCNN path-planning algorithm offers significant speed improvements for robotics and automated driving.
  • The method effectively plans optimal paths, considering both cost and shortest distance.
  • This approach provides a robust and efficient solution for path planning in diverse grid map environments.