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Robots are now widely used in product disassembly lines, which significantly improves end-of-life (EOL) product disassembly efficiency. Most of the current research on disassembly line balancing problems focuses on decomposing one product. More than one product can be disassembled concurrently, which can further improve the efficiency. Moreover, uncertainty such as the depreciation of EOL products, may result in disassembly failure. In this research, a stochastic multi-product robotic disassembly line balancing model is established using an AND/OR graph. It takes the precedence relationship, cycle constraint, and disassembly failure into consideration to maximize the profit and minimize the energy consumption for disassembling multiple products. A Pareto-improved multi-objective brainstorming optimization algorithm combined with stochastic simulation is proposed to solve the problem. Furthermore, by conducting experiments on some real cases and comparing with four state-of-the-art multi-objective optimization algorithms, i.e., the multi-objective discrete gray wolf optimizer, artificial bee colony algorithm, nondominated sorting genetic algorithm II, and multi-objective evolutionary algorithm based on decomposition, this paper validates its excellent performance in solving the concerned problem. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the degradation of particle weight and the loss of particle diversity in FastSLAM 2.0 algorithm led to the decline of robot positioning and mapping accuracy, this paper proposed an improved FastSLAM 2.0 algorithm based on brain storm optimization. Firstly, it took the particle weight after importance sampling as the moderation value of individual evaluation in the brainstorming algorithm, and completed K-means clustering operation according to the difference of the moderation value. Secondly, it mutated the set after clustering to cancel the individual selection operation in the brainstorm algorithm. In this way, the improved brainstorm algorithm could replace the resampling process of particle filter, effectively alleviate the particle dilution phenomenon, increase the particle diversity, and finally improved the positioning and mapping accuracy of the rescue robot. In the robot positioning mapping experiment, compared with the classic FastSLAM 2.0 algorithm and the improved FastSLAM 2.0 algorithm based on genetic algorithm, the proposed algorithm has the highest positioning accuracy. Compared with the classic FastSLAM 2.0 algorithm, the positioning accuracy of the proposed algorithm is improved by 63%, and the stability is improved by 55%. [ABSTRACT FROM AUTHOR]