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<p>Motion planning for multi-degree-of-freedom serial manipulators in manufacturing environments presents significant challenges due to the multitude of variables and operational constraints involved. This study introduces an innovative multi-objective optimization (MOO) strategy that employs fuzzy particle swarm optimization (FPSO). By integrating fuzzy logic with particle swarm optimization (PSO), FPSO allows dynamic parameter adjustments, thereby enhancing adaptability and convergence in complex search spaces. The basic PSO, which often face issues of premature convergence or static parameter limitations, FPSO achieves an optimal balance between minimizing cycle time (~ 0.34&#xa0;s) and energy consumption (~ 230.45&#xa0;W&#xa0;s), while adhering to strict positional accuracy (≤ 0.001&#xa0;mm) and dynamic constraints (e.g., joint torques). The proposed method demonstrated superior performance, surpassing basic PSO by approximately 5–10% in terms of the objective function value, thus highlighting its enhanced efficiency and sustainability. These findings emphasize FPSO’s potential of FPSOs to significantly improve decision-making processes in advanced manufacturing systems.</p> Graphical abstract: <p>Methodology for motion planning of multi degree serial manipulator with dynamic constraints using fuzzy PSO</p>

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Motion planning of multi degree serial manipulator with dynamic constraints using fuzzy particle swarm optimization

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • V. B. Shinde,
  • P. J. Pawar

摘要

Motion planning for multi-degree-of-freedom serial manipulators in manufacturing environments presents significant challenges due to the multitude of variables and operational constraints involved. This study introduces an innovative multi-objective optimization (MOO) strategy that employs fuzzy particle swarm optimization (FPSO). By integrating fuzzy logic with particle swarm optimization (PSO), FPSO allows dynamic parameter adjustments, thereby enhancing adaptability and convergence in complex search spaces. The basic PSO, which often face issues of premature convergence or static parameter limitations, FPSO achieves an optimal balance between minimizing cycle time (~ 0.34 s) and energy consumption (~ 230.45 W s), while adhering to strict positional accuracy (≤ 0.001 mm) and dynamic constraints (e.g., joint torques). The proposed method demonstrated superior performance, surpassing basic PSO by approximately 5–10% in terms of the objective function value, thus highlighting its enhanced efficiency and sustainability. These findings emphasize FPSO’s potential of FPSOs to significantly improve decision-making processes in advanced manufacturing systems.

Graphical abstract:

Methodology for motion planning of multi degree serial manipulator with dynamic constraints using fuzzy PSO