Single phase multi-view clustering using unified graph learning and spectral representation

Dornaika, Fadi; Hajjar, S. El

doi:10.1016/j.ins.2023.119366

Cited by 7 publications

(1 citation statement)

References 36 publications

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“…This can take into account the advantages of the long running time of the mobile robot and the wide vision of the UAV. In addition, clustering and deep learning can be further combined in this process, for example, [44][45][46].…”

Section: Discussionmentioning

confidence: 99%

Cooperative Coverage Path Planning for Multi-Mobile Robots Based on Improved K-Means Clustering and Deep Reinforcement Learning

Ni,

Gu,

Tang

et al. 2024

Electronics

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With the increasing complexity of patrol tasks, the use of deep reinforcement learning for collaborative coverage path planning (CPP) of multi-mobile robots has become a new hotspot. Taking into account the complexity of environmental factors and operational limitations, such as terrain obstacles and the scope of the task area, in order to complete the CPP task better, this paper proposes an improved K-Means clustering algorithm to divide the multi-robot task area. The improved K-Means clustering algorithm improves the selection of the first initial clustering point, which makes the clustering process more reasonable and helps to distribute tasks more evenly. Simultaneously, it introduces deep reinforcement learning with a dueling network structure to better deal with terrain obstacles and improves the reward function to guide the coverage process. The simulation experiments have confirmed the advantages of this method in terms of balanced task assignment, improvement in strategy quality, and enhancement of coverage efficiency. It can reduce path duplication and omission while ensuring coverage quality.

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Section: Discussionmentioning

confidence: 99%