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Computer Vision Positioning and Local Obstacle Avoidance Optimization Based on Neural Network Algorithm.

Lei Yang1, Weimin Lei1,2

  • 1School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China.

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This study enhances mobile robot navigation by optimizing neural network algorithms for computer vision positioning and local obstacle avoidance. The research introduces novel methods for obstacle data acquisition and semantic image segmentation, improving autonomous navigation accuracy and speed.

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Indoor positioning for mobile robots is crucial for autonomous navigation, but GPS is ineffective indoors.
  • Existing indoor positioning technologies face challenges like base station deployment and latency.
  • Computer vision offers a cost-effective and robust alternative for indoor robot positioning.

Purpose of the Study:

  • To systematically optimize neural network algorithms for computer vision positioning in mobile robots.
  • To improve local obstacle avoidance accuracy and speed for enhanced autonomous navigation.
  • To develop advanced methods for obstacle data acquisition and semantic image segmentation.

Main Methods:

  • Optimized neural network algorithms for computer vision positioning.
  • Developed an obstacle data acquisition method using VGG16 and fast RCNN.
  • Designed a VGG16-combined semantic image segmentation algorithm for pixel-level classification and path boundary detection.

Main Results:

  • Achieved systematic optimization of neural network algorithms for robot vision positioning.
  • Successfully implemented an obstacle data acquisition method enhancing local obstacle avoidance.
  • Improved the accuracy and speed of semantic image segmentation for robot path planning.

Conclusions:

  • Neural network optimization significantly advances computer vision positioning for mobile robots.
  • The proposed methods enhance local obstacle avoidance, contributing to more reliable autonomous navigation.
  • This research promotes the development of highly automated and accurate mobile robot systems.