Optimal non-autonomous area coverage control with adaptive reinforcement learning

Soleymani, Farzan; Miah, Suruz; Spinello, Davide

doi:10.1016/j.engappai.2023.106068

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…Furthermore, ref. [61] utilized a reinforcement learning framework to craft controllers for multi-coverage robots. The reduction in coverage quantity, delineated in (14), serves as the reward function.…”

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confidence: 99%

Applications of Voronoi Diagrams in Multi-Robot Coverage: A Review

Zhou,

Li,

Wang

et al. 2024

JMSE

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In recent decades, multi-robot region coverage has played an important role in the fields of environmental sensing, target searching, etc., and it has received widespread attention worldwide. Due to the effectiveness in segmenting nearest regions, Voronoi diagrams have been extensively used in recent years for multi-robot region coverage. This paper presents a survey of recent research works on region coverage methods within the framework of the Voronoi diagram, to offer a perspective for researchers in the multi-robot cooperation domain. First, some basic knowledge of the Voronoi diagram is introduced. Then, the region coverage issue under the Voronoi diagram is categorized into sensor coverage and task execution coverage problems, respectively, considering the sensor range parameter. Furthermore, a detailed analysis of the application of Voronoi diagrams to the aforementioned two problems is provided. Finally, some conclusions and potential further research perspectives in this field are given.

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confidence: 99%