What question did this study set out to answer?

Develop an improved sparrow search algorithm to solve convergence traps, low efficiency, and mechanical shock during multi-objective trajectory planning for robotic manipulators.

October 17, 2025Mechanical sciencesOpen Access

Integrating elite opposition-based learning and Cauchy–Gaussian mutation into sparrow search algorithm for time–impact collaborative trajectory optimization of robotic manipulators

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Authors

YWYue WangChengdu University of Technology RLRongguang LeiBeihua University MWMeng WangXihua University

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Implication

Simulation study demonstrates enhanced speed and reduced mechanical impact in an industrial robotic arm, indicating improved precision and stability for automated manufacturing.

Key Points

Develop an improved sparrow search algorithm to solve convergence traps, low efficiency, and mechanical shock during multi-objective trajectory planning for robotic manipulators.
Constructed a new improved sparrow search algorithm (NISSA) incorporating elite reverse learning and Cauchy–Gaussian mutation strategies.
Modeled joint spatial trajectories via 3–5–3 polynomial interpolation and established a dynamic-weight objective function balancing trajectory time and mechanical shock.
Evaluated algorithm performance using kinematic simulation of an IRB4600 six-axis robotic arm compared to standard SSA and MIPSO baselines.
NISSA shortened trajectory planning time by 19.6% and decreased path redundancy by 25.7% relative to comparative algorithms.
Iterative convergence speed increased by 68.75% under NISSA optimization.
The standard deviation of joint acceleration was reduced to 28.5% of its baseline value, significantly lowering mechanical shock.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/6a7eb97ea11f4f846b8ec8f8 https://doi.org/10.5194/ms-16-533-2025

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Implication

Simulation study demonstrates enhanced speed and reduced mechanical impact in an industrial robotic arm, indicating improved precision and stability for automated manufacturing.

Integrating elite opposition-based learning and Cauchy–Gaussian mutation into sparrow search algorithm for time–impact collaborative trajectory optimization of robotic manipulators

Discussion

Member takes

Implication

Key Points

Cite This Study

Integrating elite opposition-based learning and Cauchy–Gaussian mutation into sparrow search algorithm for time–impact collaborative trajectory optimization of robotic manipulators

Discussion

Member takes

Implication

Key Points

Cite This Study

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