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<p>The advent of automation in transportation technology plays a crucial role in revolutionizing modern logistics systems. This research focuses on the design, modeling, and performance analysis of a LuggAge Carrying BoT (LABO) that combines kinematic modeling and statistical data analysis to predict path trajectories. The LABO system incorporates an advanced adjoint transformation model, distinct from conventional Point of Entry formula models, to establish precise path calibration and compensate for load-induced deviations. In autonomous mode, a BOX-Ljung statistical model predicts path trajectories by analyzing speed, payload, and positional data. Experimentally, LABO demonstrated optimal performance, carrying a payload of up to 14&#xa0;kg at a maximum speed of 0.24&#xa0;m/s on rough surfaces. The motor driver configuration was tested under varying loads, showing a decline in speed from 0.3&#xa0;m/s at 2&#xa0;kg payload to 0.25&#xa0;m/s at 12&#xa0;kg payload. Kinematic analysis was performed using a serial configuration with a 20-degree-of-freedom model, yielding a 0.27% improvement in path precision when compared to Denavit–Hartenberg models. The proposed LABO system offers a reliable, user-friendly, and cost-effective solution for transporting heavy luggage, significantly reducing the physical burden on elderly or disabled individuals, while the BOX-Ljung model ensures precise path prediction for improved autonomous navigation.</p>

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Self Path Trajectory Sensing Analysis by Luggage Carrying Bot (Labo) Using Box-Statistics Path Predictor

  • 期刊论文

  • 发表日期：

    2025年4月5日

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  • E. B. Priyanka,
  • S. Thangavel,
  • T. Surendran

摘要

The advent of automation in transportation technology plays a crucial role in revolutionizing modern logistics systems. This research focuses on the design, modeling, and performance analysis of a LuggAge Carrying BoT (LABO) that combines kinematic modeling and statistical data analysis to predict path trajectories. The LABO system incorporates an advanced adjoint transformation model, distinct from conventional Point of Entry formula models, to establish precise path calibration and compensate for load-induced deviations. In autonomous mode, a BOX-Ljung statistical model predicts path trajectories by analyzing speed, payload, and positional data. Experimentally, LABO demonstrated optimal performance, carrying a payload of up to 14 kg at a maximum speed of 0.24 m/s on rough surfaces. The motor driver configuration was tested under varying loads, showing a decline in speed from 0.3 m/s at 2 kg payload to 0.25 m/s at 12 kg payload. Kinematic analysis was performed using a serial configuration with a 20-degree-of-freedom model, yielding a 0.27% improvement in path precision when compared to Denavit–Hartenberg models. The proposed LABO system offers a reliable, user-friendly, and cost-effective solution for transporting heavy luggage, significantly reducing the physical burden on elderly or disabled individuals, while the BOX-Ljung model ensures precise path prediction for improved autonomous navigation.