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Optimal Robotic Assembly Sequence Generation Using Particle Swarm Optimization

M. V. A. Raju. Bahubalendruni, B. B. Biswal, and B. B. V. L Deepak
Dept. of Industrial Design, National Institute of Technology- Rourkela, Odisha-769008, India
Abstract—The optimal feasible robotic assembly sequence leads to efficient manufacturing process by minimizing the assembly cost. Assembly cost is based on the energy required to assemble the components through collision free path and robot directional changes during the assembly operations. So, the determination of a feasible assembly sequence with minimum assembly cost is vital concern for manufacturing industries. Through obtaining optimal assembly sequences taking user inputs (assembly connection matrix, precedence relations, etc.,) is less complicate, the correctness of methodology depends on the skill of the engineer who supply these inputs. The present research aims to explore PSO based methodology to determine cost effective optimal robotic assembly sequence through CAD product. The integration of PSO with CAD environment ensures the correctness and completeness of the methodology. The methods to interface with the CAD data to extract liaison data, to test for liaison predicate and feasibility predicate is presented and analyzed briefly with an example. In this methodology, each component of the assembled product is considered as the particle (bird) and mutation operation is performed to generate a new assembly sequence for each iteration. To generate optimal assembly sequence, a fitness function is generated, which is based on the energy function and robot directional changes associated with assembly sequence. The sequence which is having the best fitness value is treated as the optimal robotic assembly sequence.

Index Terms—robotic assembly sequence, particle swarm optimization, optimal assembly sequence

Cite: M. V. A. Raju. Bahubalendruni, B. B. Biswal, and B. B. V. L Deepak, "Optimal Robotic Assembly Sequence Generation Using Particle Swarm Optimization," Jounal of Automation and Control Engineering, Vol. 4, No. 2, pp. 89-95, April, 2016. doi: 10.12720/joace.4.2.89-95
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