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Related Experiment Video

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Path Tracking Control of Field Information-Collecting Robot Based on Improved Convolutional Neural Network Algorithm.

Yili Gu1, Zhiqiang Li1, Zhen Zhang1

  • 1College of Engineering, Anhui Agricultural University, Hefei 230036, China.

Sensors (Basel, Switzerland)
|February 7, 2020
PubMed
Summary
This summary is machine-generated.

A novel robot collects corn interline information, overcoming challenges in dense fields. Its advanced AI and dynamic modeling ensure stable navigation for improved crop management and pest control.

Keywords:
controlcorn rowsinformation collection robotmachine visionpath tracking

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Area of Science:

  • Agricultural Robotics
  • Artificial Intelligence in Agriculture
  • Precision Farming Technologies

Background:

  • Narrow corn row spacing limits machinery access and real-time growth observation in later growth stages.
  • Obstructions from branches, leaves, and weeds exacerbate difficulties in monitoring and managing corn crops.

Purpose of the Study:

  • To design and develop a robot for efficient corn interline information collection.
  • To address challenges related to machinery access and real-time crop monitoring in cornfields.

Main Methods:

  • Developed a robot with a mathematical model and a control system.
  • Utilized an improved convolutional neural network for path planning via corn rhizome detection.
  • Employed RecurDyn/track for dynamic modeling in soft soil and MATLAB/SIMULINK for control system simulation.

Main Results:

  • The sliding-mode variable structure control method demonstrated superior control performance.
  • Simulations and field experiments confirmed the robot's stable operation with minimal deviation.
  • The developed robot effectively navigates corn interlines for data collection.

Conclusions:

  • The designed robot is a viable solution for real-time corn growth monitoring.
  • It supports applications in field plant protection and disease/pest control.
  • The robot facilitates human-machine separation in agricultural operations.