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<p>This paper introduces a novel pure pursuit algorithm for path following non-holonomic-wheeled mobile robots. The method is based on an improved pure pursuit algorithm, where controllers built using Hedge Algebras (HA) are integrated to regulate the robot’s linear velocity and look-ahead distance. -Algebras is a control method which constructs semantic quantification in natural language into algebraic structures. To enhance the performance of these controllers, accelerated particle swarm optimization (APSO) is applied for parameter fine-tuning, resulting in an optimized version referred to as HA-APSO (oHA-APSO). Several improvements are brought by this work: (1) A novel application of Hedge Algebras in motion control is proposed. (2) The refinement of these controllers using APSO enhances their accuracy. (3) The integration of these components into the pure pursuit algorithm results in improved path tracking. Results in both simulations and real-world conditions show that this method helps the robot handle tight turns more smoothly. Compared to several approaches, the proposed approach achieves the lowest lateral error, with values of 0.065<i>m</i> and 0.047<i>m</i> for the lemniscate and zigzag trajectories in simulation, respectively. A video showcasing the experimental results is available at: <a href="https://drive.google.com/drive/folders/1s6eYm6mTQ3n3Ht8DbCgOloPwzqpIFzLL?usp=sharing">https://ddatt.github.io/oHAPP-Tracking/</a>.</p>

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The development of an optimal hedge algebras path following controller for Robotnik

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Thi-Thoa Mac,
  • Trieu-Duong Vu,
  • Quang-Dat Bui,
  • Minh-Quan Le,
  • Sy-Tai Nguyen,
  • Xuan-Thuan Nguyen

摘要

This paper introduces a novel pure pursuit algorithm for path following non-holonomic-wheeled mobile robots. The method is based on an improved pure pursuit algorithm, where controllers built using Hedge Algebras (HA) are integrated to regulate the robot’s linear velocity and look-ahead distance. -Algebras is a control method which constructs semantic quantification in natural language into algebraic structures. To enhance the performance of these controllers, accelerated particle swarm optimization (APSO) is applied for parameter fine-tuning, resulting in an optimized version referred to as HA-APSO (oHA-APSO). Several improvements are brought by this work: (1) A novel application of Hedge Algebras in motion control is proposed. (2) The refinement of these controllers using APSO enhances their accuracy. (3) The integration of these components into the pure pursuit algorithm results in improved path tracking. Results in both simulations and real-world conditions show that this method helps the robot handle tight turns more smoothly. Compared to several approaches, the proposed approach achieves the lowest lateral error, with values of 0.065m and 0.047m for the lemniscate and zigzag trajectories in simulation, respectively. A video showcasing the experimental results is available at: https://ddatt.github.io/oHAPP-Tracking/.