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Snake optimizer LSTM-based UWB positioning method for unmanned crane.

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This study introduces a novel Ultra-Wide-Band (UWB) positioning method for unmanned crane systems, using a Snake Optimizer Long Short-Term Memory (SO-LSTM) framework to precisely track the crane hook's location.

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Area of Science:

  • Robotics and Automation
  • Sensor Technology
  • Machine Learning

Background:

  • Traditional crane positioning methods struggle with load swinging.
  • Accurate load position is crucial for unmanned and intelligent crane operations.
  • Existing techniques are limited to main carriage and trolley positioning.

Purpose of the Study:

  • To develop a novel Ultra-Wide-Band (UWB) positioning method for unmanned crane systems.
  • To achieve real-time and precise localization of the crane hook.
  • To overcome the limitations of traditional positioning techniques in dynamic environments.

Main Methods:

  • Implementation of a multi-base station and multi-tag UWB system.
  • Utilizing Time Division Multiple Access (TDMA) and Two-Way Ranging (TWR) for distance measurement.
  • Optimization of Long Short-Term Memory (LSTM) network hyperparameters with the Snake Optimizer (SO) algorithm.

Main Results:

  • The SO-LSTM method achieved a maximum positioning error of 0.1125 meters and RMSE of 0.0589 meters.
  • Significant reduction in Root Mean Square Error (RMSE) by 63.39% compared to Least Squares (LS).
  • Significant reduction in Maximum Positioning Error (MPE) by 60.77% compared to LS.

Conclusions:

  • The proposed SO-LSTM UWB positioning method offers superior accuracy and effectiveness for unmanned crane systems.
  • This approach significantly outperforms conventional LS and Kalman Filter (KF) methods.
  • Enables precise real-time load positioning, enhancing crane automation and safety.