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Improved Position Estimation Algorithm of Agricultural Mobile Robots Based on Multisensor Fusion and Autoencoder

Peng Gao1,2,3, Hyeonseung Lee2,3, Chan-Woo Jeon4

  • 1College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
Summary

Accurate positioning for agricultural mobile robots (AMRs) is essential. A new multisensor fusion algorithm using an autoencoder neural network improves AMR location accuracy, even with signal interference or sensor failures.

Keywords:
Kalman filter (KF)agricultural mobile robots (AMRs)autoencoder neural networkglobal navigation satellite system (GNSS)inertial measurement unit (IMU)

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Sensor Fusion

Background:

  • High-precision positioning is critical for agricultural mobile robots (AMRs) to execute control commands.
  • Global Navigation Satellite System (GNSS) and Real-Time Kinematic GNSS (RTK-GNSS) offer precision but degrade with signal obstruction.
  • Existing methods struggle with signal interference and sensor malfunctions, impacting AMR operational reliability.

Purpose of the Study:

  • To develop an advanced position estimation algorithm for AMRs.
  • To enhance AMR positioning accuracy and robustness against environmental challenges and sensor failures.
  • To optimize the Extended Kalman Filter (EKF) using neural networks for improved state and measurement modeling.

Main Methods:

  • Implemented a multisensor fusion approach combining RTK-GNSS, inertial measurement unit (IMU), and dual-rotary encoder data.
  • Utilized an Extended Kalman Filter (EKF) for data fusion.
  • Employed an autoencoder and radial basis function (ARBF) neural network to optimize EKF noise matrices and model state/measurement equations.
  • Conducted static and dynamic experiments in various environments (road, grass, field) and simulated sensor failures.

Main Results:

  • The proposed algorithm significantly improved positioning estimation accuracy compared to RTK-GNSS across all tested environments.
  • The system demonstrated robustness, maintaining enhanced accuracy even during RTK-GNSS signal interference or rotary encoder failures.
  • Static and dynamic experiments validated the algorithm's performance using Circular Error Probability (CEP) and twice the distance root mean squared (2dRMS) criteria.

Conclusions:

  • The multisensor fusion algorithm integrated with an ARBF neural network effectively enhances AMR positioning accuracy.
  • The developed system offers improved robustness and reliability for AMRs operating in challenging conditions.
  • This approach represents a significant advancement in AMR position prediction and navigation capabilities.