Assembly space optimization for linear actuator assembly process based on PSO: two-robot case

A particle swarm optimization (PSO) algorithm-based analysis and optimization method for minimizing the assembly space of a workcell which is composed of 2 robot arms and 3 fixtures is proposed. By analysing the effect of population and acceleration coefficients on the fitness values in PSO, the most suitable set of algorithm parameters for optimising the size of the assembly space is identified. By applying the optimal parameters to PSO algorithm and adding a boundary check and a path accessibility check for shaped fixtures, the minimum fitness value, which is the ratio of the minimum assembly space to the total assembly workcell, and the polar position parameters of each layout object are derived. The feasibility of the layout is verified by simulation in RoboDK. A set of PSO parameters appropriate for solving the 2-robot arms assembly space minimization problem is determined, a path accessibility check for shaped fixtures is established, a layout for minimizing the assembly space of a workcell composed of 2 robot arms and 3 fixtures is derived, and the feasibility of the conclusions is verified through simulation. The analysis and optimization method of the multi-robot arm assembly space minimization problem based on the PSO algorithm is obtained. This research is an exploration of a new optimisation objective in the field of workcell layout, the minimisation of the assembly space of multiple robotic arms and has led to an analytical and optimisation method based on PSO algorithm for solving this type of problem.

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Published
2024-12-06
DOI
https://doi.org/10.1109/ricai64321.2024.10911523
Citations
1
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
0.35
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article

Assembly space optimization for linear actuator assembly process based on PSO: two-robot case

Zhiyao Liu, P. Ma, Rui Xu, Yuanzheng Zhang
1 citations
Manufacturing Process and Optimization
0.35
article

Assembly space optimization for linear actuator assembly process based on PSO: two-robot case

Zhiyao Liu, P. Ma, Rui Xu, Yuanzheng Zhang
article en
1 citations

Abstract

A particle swarm optimization (PSO) algorithm-based analysis and optimization method for minimizing the assembly space of a workcell which is composed of 2 robot arms and 3 fixtures is proposed. By analysing the effect of population and acceleration coefficients on the fitness values in PSO, the most suitable set of algorithm parameters for optimising the size of the assembly space is identified. By applying the optimal parameters to PSO algorithm and adding a boundary check and a path accessibility check for shaped fixtures, the minimum fitness value, which is the ratio of the minimum assembly space to the total assembly workcell, and the polar position parameters of each layout object are derived. The feasibility of the layout is verified by simulation in RoboDK. A set of PSO parameters appropriate for solving the 2-robot arms assembly space minimization problem is determined, a path accessibility check for shaped fixtures is established, a layout for minimizing the assembly space of a workcell composed of 2 robot arms and 3 fixtures is derived, and the feasibility of the conclusions is verified through simulation. The analysis and optimization method of the multi-robot arm assembly space minimization problem based on the PSO algorithm is obtained. This research is an exploration of a new optimisation objective in the field of workcell layout, the minimisation of the assembly space of multiple robotic arms and has led to an analytical and optimisation method based on PSO algorithm for solving this type of problem.

Dyson (United Kingdom) (GB), Imperial College London (GB), Huazhong University of Science and Technology (CN), University of Birmingham (GB)
Affordable and clean energy
Openalex Percentile: Top 30%
Manufacturing Process and Optimization
0.35
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Assembly space optimization for linear actuator assembly process based on PSO: two-robot case — Zhiyao Liu, P. Ma, et al. · (2024) | TGRS Research Map | TGRS