AI Article Synopsis
- Real-time location tracking is crucial for precise navigation in agricultural robots, and this study introduces a method to improve data accuracy.
- The researchers developed a data filtering technique and an active screening model to minimize errors in position data from the Global Navigation Satellite System (GNSS).
- The new active filtering-Kalman filter algorithm significantly decreases distance deviation in positioning, enhancing the effectiveness of agricultural robot navigation and contributing to advancements in smart agriculture.
Article Abstract
Real-time acquisition of location information for agricultural robotic systems is a prerequisite for achieving high-precision intelligent navigation. This paper proposes a data filtering and combined positioning method, and establishes an active screening model. The dynamic and static positioning drift points of the carrier are eliminated or replaced, reducing the complexity of the original Global Navigation Satellite System (GNSS) output data in the positioning system. Compared with the traditional Kalman filter combined positioning method, the proposed active filtering-Kalman filter algorithm can reduce the maximum distance deviation of the carrier along a straight line from 0.145 m to 0.055 m and along a curve from 0.184 m to 0.0640 m. This study focuses on agricultural robot positioning technology, which has an important influence on the development of smart agriculture.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11014096 | PMC |
| http://dx.doi.org/10.3390/s24072372 | DOI Listing |
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