An autonomous ore packing system through deep reinforcement learning

Ren, He; Zhong, Rui

doi:10.1016/j.asr.2024.01.061

Cited by 3 publications

(1 citation statement)

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“…When focusing on packaging optimization problems, several examples in literature exist that explore the efficacy of artificial intelligence as a value tool in addressing the diverse array of packaging optimization challenges. For example, deep reinforcement learning approaches have been shown for solving the rectangular strip packaging problem by Fang and Rao [31], for an autonomous ore packing system by Ren and Zhong [32], and the optimal vehicle packing space optimization with a focus on packing sequence of items by Tian and Kang [33]. Like the reliance on tuning parameters of the previously discussed methods, the literature has shown that a primary drawback of these deep learning is the reliance on training data, which may not be readily available for a given type of packaging problem.…”

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confidence: 99%

Packing optimization of practical systems using a dynamic acceleration methodology

Douglas,

Huh,

Jun

et al. 2024

J. Eng. Appl. Sci.

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System design is a challenging and time-consuming task which often requires close collaboration between several multidisciplinary design teams to account for complex interactions between components and sub-systems. As such, there is a growing demand in industry to create better performing, efficient, and cost-effective development tools to assist in the system design process. Additionally, the ever-increasing complexity of systems today often necessitates a shift away from manual expertise and a movement towards computer-aided design tools. This work narrows the scope of the system design process by focusing on one critical design aspect: the packaging of system components. The algorithm presented in this paper was developed to optimize the packaging of system components with consideration of practical, system-level functionalities and constraints. Using a dynamic acceleration methodology, the algorithm packages components from an initial position to a final packed position inside of a constrained volume. The motion of components from initial to final positions is driven by several acceleration forces imposed on each component. These accelerations are based on physical interactions between components and their surrounding environment. Various system-level performance metrics such as center of mass alignment and rotational inertia reduction are also considered throughout optimization. Results of several numerical case studies are also presented to demonstrate the functionality and capability of the proposed packaging algorithm. These studies include packaging problems with known optimal solutions to verify the efficacy of the algorithm. Finally, the proposed algorithm was used in a more practical study for the packaging of an urban air mobility nacelle to demonstrate the algorithm’s prospective capabilities in solving real-world packaging problems.

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confidence: 99%