Journal of System Simulation
Abstract
It is difficult for single-objective trajectory planning methods to meet the requirements of precision, diversity and complexity of robotic arms. A trajectory planning model based on an improved multi-objective differential evolution algorithm (guided multi-objective differential evolution, GMODE) algorithm is proposed. Cubic polynomial interpolation and B-spline curves are employed to construct multi-objective functions, while GMODE is adopted to overcome the limitations of traditional algorithms, such as insufficient population diversity, the tendency to fall into local optima, and slow convergence. A grouping strategy, parameter generation mechanism, and elite mutation based on fuzzy Cmeans clustering are introduced to optimize B-spline control nodes. Simulation experiments demonstrate that the proposed method achieves excellent performance in terms of time, energy, and impact. Furthermore, the incorporation of adaptive weights significantly enhances multi-objective performance.
Recommended Citation
Liu, Manqiang and Shang, Ziqiang
(2026)
"Application of Improved Multi-objective Differential Algorithm in Robotic Arm Multi-objective Trajectory Planning,"
Journal of System Simulation: Vol. 38:
Iss.
6, Article 13.
DOI: 10.16182/j.issn1004731x.joss.25-0644
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol38/iss6/13
First Page
1647
Last Page
1668
CLC
TP242.2
Recommended Citation
Liu Manqiang, Shang Ziqiang. Application of Improved Multi-objective Differential Algorithm in Robotic Arm Multi-objective Trajectory Planning[J]. Journal of System Simulation, 2026, 38(6): 1647-1668.
DOI
10.16182/j.issn1004731x.joss.25-0644
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