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Related Experiment Video

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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Development of a Moving Baseline RTK/Motion Sensor-Integrated Positioning-Based Autonomous Driving Algorithm for a

Joong-Hee Han¹, Chi-Ho Park¹, Young Yoon Jang²

¹Division of Electronics & Information System, DGIST, Daegu Metropolitan City 42988, Republic of Korea.

Sensors (Basel, Switzerland)

|December 23, 2022

Summary

A new low-cost autonomous driving system for speed sprayers was developed using two global navigation satellite system (GNSS) modules and a motion sensor. This system precisely estimates yaw and achieves stable autonomous driving and spraying in orchards, enhancing safety and efficiency in pest control.

Keywords:

autonomous driving-based spraying work motion sensor moving baseline real-time kinematic sensor fusion speed sprayer

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Area of Science:

Agricultural Engineering
Robotics
Geomatics Engineering

Background:

Pesticide application poses risks of poisoning and accidents.
Efficient and safe pest control requires automated solutions like autonomous speed sprayers.
Existing autonomous systems can be prohibitively expensive for widespread adoption.

Purpose of the Study:

To develop a cost-effective autonomous driving system for speed sprayers.
To enhance the safety and efficiency of agricultural pest control operations.
To enable commercialization of low-cost autonomous speed sprayers.

Main Methods:

Developed a moving baseline (MB) real-time kinematic (RTK)/motion sensor-integrated positioning algorithm using a loosely coupled extended Kalman filter.
Integrated two low-cost global navigation satellite system (GNSS) modules and a low-cost motion sensor.
Created an autonomous driving-based spraying algorithm utilizing the developed positioning algorithm.

Main Results:

The MB RTK/motion sensor-integrated positioning algorithm achieved a yaw precision of 0.14° in static tests, significantly outperforming GNSS RTK/motion sensor integration (4.53°).
The system demonstrated stable autonomous driving and spraying in an orchard, with a path-following error root mean square (RMS) of 0.06 m.
Precise yaw estimation was maintained even with electromagnetic interference from the spraying device.

Conclusions:

The developed low-cost positioning and autonomous driving algorithms enable stable and precise operation of speed sprayers.
This technology contributes to the commercialization of affordable autonomous agricultural machinery.
The system enhances safety and efficiency in pest control, reducing risks associated with manual operations.