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<p>Automated guided vehicles (AGVs) are intelligent robotic systems that play a crucial role in applications, such as transportation, food delivery, and medical emergencies. One of the primary challenges in AGV deployment is achieving precise navigation to ensure task reliability, safety, and efficient battery consumption along predetermined routes. Vision-based methods have gained significant attention for their high performance in robot navigation. However, selecting the most effective algorithm with minimal sensor use remains an active area of research. This study introduces a novel and efficient fusion-based navigation method, termed the Extended Fusion 1 Method (EFM1), which integrates data from camera and infrared (IR) sensors. The system leverages feature-based algorithms, such as SIFT, ORB, FAST, SURF, BRISK, and BRIEF, to improve path-tracking accuracy. The main objective of this fusion approach is to enhance navigational precision and identify the most suitable algorithm for robust AGV path-tracking. The EFM1 method is simulated and validated using the CoppeliaSim (V-REP) simulator, incorporating the real-world dimensions of the previously developed AGV model, Hongma, via Python API. The simulation evaluates six feature-based algorithms across five distinct path types: circular, elliptical, spiral, figure-eight, and custom path. Performance is assessed in terms of maximum achievable speed, minimal path-tracking error, body orientation accuracy, and simulation time. Statistical analysis, including inferential techniques and post hoc tests, is used to interpret the results. The experimental findings demonstrate that the proposed EFM1 algorithm outperforms traditional vision-only approaches in effectively tracking all five path types, confirming its potential for reliable and efficient AGV navigation. The proposed EFM1 algorithm integrates vision and IR sensors, enhancing AGV navigation accuracy by up to 84% while using minimal sensors. It outperforms previous methods by delivering up to 22.2% faster simulation times, significantly reduced error rates (for example, ORB error decreased by 93%), and increased speed across five test paths. The comparison between investigated methods shows that FAST excels on dynamic paths with notable speed improvements, while SURF performs better on complex trajectories as confirmed by statistical analysis. Although EFM1 improves most metrics, body orientation changes increased, indicating a trade-off between agility and movement stability.</p>

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Comparative performance analysis of a novel fusion-based algorithm for AGV navigation

  • 期刊论文

  • 发表日期：

    2025年10月29日

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  • Ata Jahangir Moshayedi,
  • Dangling Xu,
  • Maryam Sharifdoust,
  • Amir Sohail Khan,
  • Zeashan Hameed Khan,
  • Mehran Emadi Andani

摘要

Automated guided vehicles (AGVs) are intelligent robotic systems that play a crucial role in applications, such as transportation, food delivery, and medical emergencies. One of the primary challenges in AGV deployment is achieving precise navigation to ensure task reliability, safety, and efficient battery consumption along predetermined routes. Vision-based methods have gained significant attention for their high performance in robot navigation. However, selecting the most effective algorithm with minimal sensor use remains an active area of research. This study introduces a novel and efficient fusion-based navigation method, termed the Extended Fusion 1 Method (EFM1), which integrates data from camera and infrared (IR) sensors. The system leverages feature-based algorithms, such as SIFT, ORB, FAST, SURF, BRISK, and BRIEF, to improve path-tracking accuracy. The main objective of this fusion approach is to enhance navigational precision and identify the most suitable algorithm for robust AGV path-tracking. The EFM1 method is simulated and validated using the CoppeliaSim (V-REP) simulator, incorporating the real-world dimensions of the previously developed AGV model, Hongma, via Python API. The simulation evaluates six feature-based algorithms across five distinct path types: circular, elliptical, spiral, figure-eight, and custom path. Performance is assessed in terms of maximum achievable speed, minimal path-tracking error, body orientation accuracy, and simulation time. Statistical analysis, including inferential techniques and post hoc tests, is used to interpret the results. The experimental findings demonstrate that the proposed EFM1 algorithm outperforms traditional vision-only approaches in effectively tracking all five path types, confirming its potential for reliable and efficient AGV navigation. The proposed EFM1 algorithm integrates vision and IR sensors, enhancing AGV navigation accuracy by up to 84% while using minimal sensors. It outperforms previous methods by delivering up to 22.2% faster simulation times, significantly reduced error rates (for example, ORB error decreased by 93%), and increased speed across five test paths. The comparison between investigated methods shows that FAST excels on dynamic paths with notable speed improvements, while SURF performs better on complex trajectories as confirmed by statistical analysis. Although EFM1 improves most metrics, body orientation changes increased, indicating a trade-off between agility and movement stability.