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A Context-Aware Navigation Framework for Ground Robots in Horticultural Environments.

Peiqi Jin1, Tongxiang Li2, Yaoqiang Pan1

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Summary
This summary is machine-generated.

This study introduces an autonomous mapping and navigation system for gardening robots using LiDAR SLAM. The method enhances semantic information extraction and achieves efficient path planning for safe and smooth robot operation in orchards.

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Area of Science:

  • Robotics
  • Computer Vision
  • Agricultural Engineering

Background:

  • Robot automation in agriculture relies on accurate environmental mapping and navigation.
  • Existing methods face challenges in processing complex orchard environments efficiently.

Purpose of the Study:

  • To develop an autonomous mapping and navigation method for gardening robots in complex environments.
  • To improve semantic information extraction and path planning efficiency for agricultural robots.

Main Methods:

  • Proposed a LiDAR SLAM-based semantic mapping algorithm for analyzing point cloud data and generating roadmaps.
  • Developed a general robot navigation framework incorporating global shortest path planning and local terrain-aware path optimization.
  • Implemented and evaluated the system on a differential drive robotic platform in an apple orchard.

Main Results:

  • The semantic mapping algorithm significantly improved information extraction efficiency and reduced feature extraction time to 0.1681s with an MIoU of 0.812.
  • Global path planning achieved a 100% success rate with an average runtime of 4ms.
  • Local path planning generated safe and smooth trajectories with an average runtime of 36ms.

Conclusions:

  • The proposed method effectively processes orchard environmental information for autonomous robot operation.
  • The system demonstrates superior performance in semantic information extraction and path planning compared to existing approaches.
  • This advancement contributes to safer and more efficient robotic automation in agricultural settings.