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Time-optimal trajectory planning for robotic manipulators based on an improved pufferfish optimization algorithm
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AI
DOI：10.3389/fmech.2026.1823141.png)
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Abstract
En 中文
Time-optimal trajectory planning is an important topic in robotic manipulation and automated system operation, particularly when joint motion must satisfy predefined velocity and acceleration constraints. This paper presents a trajectory planning approach that combines a 4-3-4 hybrid polynomial interpolation method with an improved pufferfish optimization algorithm (IPOA). The main contributions are two-fold: (1) a chaotic mapping initialization strategy based on the Logistic map to enhance population diversity, and (2) an adaptive parameter mechanism that adjusts particle movement using the mean position of superior individuals. Unlike existing metaheuristic trajectory planners that directly apply standard PSO, WOA, or GA variants, the proposed IPOA introduces these two mechanisms specifically tailored for the high-dimensional, constrained 4-3-4 polynomial time-optimization problem, providing both theoretical motivation and empirical superiority over POA and other state-of-the-art algorithms. Benchmark function experiments indicate that, under the evaluated settings, IPOA achieves faster convergence and better stability than several commonly used metaheuristic algorithms such as POA, PSO, ZOA, GWO, and WOA. When applied to a six-degree-of-freedom robotic manipulator, the method generates joint angle, velocity, and acceleration trajectories that remain smooth and continuous, without abrupt variations, and consistent with the imposed motion limits. Experiments carried out on an actual robotic platform further show that the planned trajectories can be executed reliably and reproduce the expected motion behavior. These results suggest that IPOA provides a feasible and effective method for addressing constrained, time-related trajectory optimization problems in robotic and automated applications.
Keywords:
adaptive parameters
chaotic mapping
improved pufferfish optimization algorithm (IPOA)
intelligent optimization
trajectory planning
Journal
F
IF:
3
Papers:
143
Citations:
0