An integrated control strategy for simultaneous robot assignment, tool change and preventive maintenance scheduling using Heterogeneous Graph Neural Network

Bhatta, Kshitij; Chang, Qing

doi:10.1016/j.rcim.2023.102594

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…The extraction of grinding trajectories is the key to creating intelligent grinding robots [10]. In recent years, numerous scholars have utilized 3D vision technology to investigate this topic.…”

Section: Introductionmentioning

confidence: 99%

Research on the path extraction method of shoe upper grinding based on 3D vision

Zhang,

Wei,

2023

Eng. Res. Express

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Shoe upper grinding is a crucial step in the shoe manufacturing process, impacting both the bonding quality between the sole and the upper and the overall appearance of the shoe. Presently, many shoe companies rely on manual or semi-automated grinding methods for shoe uppers, resulting in low production efficiency and subpar grinding quality. This study proposes a 3D vision-based shoe upper grinding path extraction method for guiding a robotic arm to polish the shoe upper and address this issue. Initially, the RGB-D camera was used to obtain the point cloud data of the shoe surface, which was then preprocessed using point cloud filtering, smoothing and registration methods. Second, by combining RGB images with 3D point clouds, we propose a point-cloud region of interest (ROI) extraction method based on RGB information to obtain the feature point cloud of the shoe upper grinding line. Finally, we slice the feature point cloud of the shoe upper grinding line, extract the grinding points using an intersection algorithm, and fit them with a non-uniform rational B-spline curve (NURBS) to obtain the shoe’s upper grinding path. The research demonstrates that this method can effectively extract the shoe upper grinding trajectory curve, significantly reduce the time required to obtain the shoe upper grinding trajectory, and achieve good accuracy, with promising application prospects.

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Section: Introductionmentioning

confidence: 99%

Research on the path extraction method of shoe upper grinding based on 3D vision

Zhang,

Wei,

2023

Eng. Res. Express

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“…In this method, the heterogeneous multiagent system is modeled as a graph structure, where the agents are represented as nodes within the graph. The communication and collaboration relationships between agents are denoted as edges in the graph [8,9] . Through the joint optimization of ACO algorithms and the neural network, this method can achieve efficient and accurate task allocation, thus improving the performance of the entire multi-agent system [10] .…”

Section: Introductionmentioning

confidence: 99%

“…The ACO algorithms have the ability for global search, enabling agents to quickly find the preliminary solution for task assignment. Their basic idea is that ants release pheromones when searching for the path and choose the next moving direction based on the pheromone concentration [8,18] . Agents simulate the cooperation process by releasing pheromones in the task allocation process and selecting task allocation strategies according to the pheromone concentration [19][20][21][22] .…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous multi-agent task allocation based on graph neural network ant colony optimization algorithms

Ma,

Gong

2023

Intelligence & Robotics

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Heterogeneous multi-agent task allocation is a key optimization problem widely used in fields such as drone swarms and multi-robot coordination. This paper proposes a new paradigm that innovatively combines graph neural networks and ant colony optimization algorithms to solve the assignment problem of heterogeneous multi-agents. The paper introduces an innovative Graph-based Heterogeneous Neural Network Ant Colony Optimization (GHNN-ACO) algorithm for heterogeneous multi-agent scenarios. The multi-agent system is composed of unmanned aerial vehicles, unmanned ships, and unmanned vehicles that work together to effectively respond to emergencies. This method uses graph neural networks to learn the relationship between tasks and agents, forming a graph representation, which is then integrated into ant colony optimization algorithms to guide the search process of ants. Firstly, the algorithm in this paper constructs heterogeneous graph data containing different types of agents and their relationships and uses the algorithm to classify and predict linkages for agent nodes. Secondly, the GHNN-ACO algorithm performs effectively in heterogeneous multi-agent scenarios, providing an effective solution for node classification and link prediction tasks in intelligent agent systems. Thirdly, the algorithm achieves an accuracy rate of 95.31% in assigning multiple tasks to multiple agents. It holds potential application prospects in emergency response and provides a new idea for multi-agent system cooperation.

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Big data for furniture intelligent manufacturing: conceptual framework, technologies, applications, and challenges

Yue,

Xiong,

et al. 2024

Int J Adv Manuf Technol

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An integrated control strategy for simultaneous robot assignment, tool change and preventive maintenance scheduling using Heterogeneous Graph Neural Network

Cited by 7 publications

References 35 publications

Research on the path extraction method of shoe upper grinding based on 3D vision

Research on the path extraction method of shoe upper grinding based on 3D vision

Heterogeneous multi-agent task allocation based on graph neural network ant colony optimization algorithms

Big data for furniture intelligent manufacturing: conceptual framework, technologies, applications, and challenges

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