Multi-Robot Repeated Area Coverage: Performance Optimization Under Various Visual Ranges

Fazli, Pooyan; Davoodi, Alireza; Mackworth, Alan K.

doi:10.1109/crv.2012.46

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…With the development of robot technology, research and interest in unmanned patrol robots have been increasing in various fields that require monitoring and security, such as social safety and national defense [20][21][22][23][24]. In particular, research on multi-object systems consisting of two or more patrol robots in a wide area is being actively conducted [25,26]. These systems were essentially trying to solve the problem of multi-robot patrols.…”

Section: Coverage For Patrol Robotsmentioning

confidence: 99%

Coverage Technology of Autonomous Mobile Mapping Robots

Lee¹

2023

Autonomous Mobile Mapping Robots

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The coverage technique is one of the essential applications of autonomous mobile mapping robots. There are various approaches for coverage depending on the model (model/non-model), robot systems (single/multi), and its purpose (patrol/cleaning). Coverage components include viewpoint generation and path planning approaches, which are described as CPP research work. Particularly, in surveillance systems, coverage techniques, such as spanning tree, cyclic coverage, and area-based coverage, are reviewed specifically, which can be expanded for multi-robot systems. In addition, required coverage techniques according to conditions for intelligent surveillance systems are summarized. Lastly, several issues on coverage, specifically cyclic coverage, are described and considered.

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Section: Coverage For Patrol Robotsmentioning

confidence: 99%

Coverage Technology of Autonomous Mobile Mapping Robots

Lee¹

2023

Autonomous Mobile Mapping Robots

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“…In MRSSs, MCPP includes an additional constraint according to the sensing range of the equipped sensors on each robot [30][31]. This is because not only the viewpoints that the robot should visit but also the area detected around them can be considered covered.…”

Section: Related Workmentioning

confidence: 99%

“…This is because not only the viewpoints that the robot should visit but also the area detected around them can be considered covered. In particular, in [30], an area consisting of obstacles with polygonal shapes was covered using cluster-based FIGURE 1. Illustration of necessity of the proposed approach in a scenario.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Coverage Area Re-Assignment Strategy for Multi-Robot Long-Term Surveillance

Lee

2023

IEEE Access

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This study deals with a new strategy of the re-assignment for multi-robot seamless coverage tasks using the concept of propagation in a multi-robot surveillance system (MRSS). In the context of MRSSs, multi-robot coverage tasks play a critical role. These tasks require generating paths for two or more robots to cover an entire area, with the objective of minimizing the time needed to complete the task. However, over time, robots may need to be excluded from coverage missions due to issues such as battery charging or malfunctions. It is important to handle these situations efficiently in order to maintain the completeness and balance of the coverage mission. Typically, it can be resolved by either recomputing the coverage algorithm for the remaining robots or redistributing the coverage task of the excluded robot to its neighbors. However, in the proposed method, the amount of coverage area of the excluded robot is equally and efficiently assigned to the remaining robots. First off, a relational graph between robots and a tree based on the excluded robot are sequentially constructed to necessarily know how the robots are geometrically arranged in the given area centered on the excluded robot. The excluded robot becomes the root of the tree, and the depth of the tree indicates the proximity of the coverage areas. Subsequently, the amount of the original coverage area of the excluded robot can be differently assigned to its nearest neighbor robots according to the size of the subtree. Then, the coverage area of the robots corresponding to the second level of the tree are added from the partial coverage area of their parent robot to keep their coverage area balanced, respectively. The similar process is continuously performed, such as 'propagation', until the re-assignment of the coverage area over the leaf nodes is complete. Finally, balanced coverage area is re-assigned to the remaining robots, which is time-efficiently computed. Simulations were performed on two occupancy grid maps that were acquired from a simultaneous localization and mapping method. The proposed method was evaluated against conventional methods on three factors such as the balanced re-assignment of the coverage area (Balancing), the variation of the individual coverage area before and after the re-assignment process (Seamless Coverage), and the total computational efficiency over time (Time-efficiency). The coverage area was uniformly re-allocated after the proposed method was applied. In addition, the proposed method had a short calculation time and enables seamless coverage even after re-allocation. In the future, probabilistic maps related to the importance rate, accident rate, and crowds in the coverage area will also be taken into consideration.

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“…Estes ambientes podem ser salas, laboratórios nos prédios no mesmo nível, plantações entre outras. O modelo de grafoé adotado em [2] para vigilância com múltiplos robôs. Locais estratégicos são destacados, possibilitando a maximização da cobertura total do ambiente em um determinado instante.…”

Section: Introductionunclassified

Uma Heurística para o Problema de Patrulhamento de Ambientes com múltiplos Agentes

Santos¹,

Massago²,

Romero³

et al. 2018

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Resumo. Neste trabalhoé proposto uma heurística para um problema de vigilância com múltiplos agentes em ambientes representados por meio de um grafo. Os nós do grafo representam os locais a serem visitados em cada ciclo. Um cicloé finalizado quando todas os locais são observados pelo menos uma vez por um agente. Deseja-se determinar as trajetórias dos agentes de modo que a duração de cada ciclo seja mínimo, que os agentes sejam incentivados a se movimentarem o máximo possível. Um modelo de programação inteira mista foi proposto bem como uma heurística baseada na decomposição do modelo. Alguns cenários foram gerados e usados para testar a abordagem heurística e os resultados obtidos mostram seu bom desempenho quando comparado a um solver comercial de otimização utilizado.Palavras-chave. Otimização inteira mista, Vigilância com múltiplos agentes, minimização ciclos, maximização visitas. IntroduçãoO termo patrulhamento ou vigilânciaé definido como a ação de percorrer uma região, em intervalos regulares, a fim de promover proteção ou supervisão da mesma. Dessa forma, para que estratégias de patrulhamento sejam eficazes, agentes (humanos ou artificiais) necessitam estabelecer uma coordenação adequada entre si, para que locais de um dado ambiente sejam visitados repetidas vezes ao longo do período de monitoramento [1]. Uma das formas de um sistema de vigilância trabalhar com um ambiente a ser observado implica no conhecimento prévio de seu ambiente, ou seja, informações de disposição e conectividade dos ambientes são fornecidas com antecedência. Por exemplo, considere um ambiente que pode ser representado por meio de um grafo de conectividade com suas 1

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Multi-Robot Repeated Area Coverage: Performance Optimization Under Various Visual Ranges

Cited by 11 publications

References 17 publications

Coverage Technology of Autonomous Mobile Mapping Robots

Coverage Technology of Autonomous Mobile Mapping Robots

An Efficient Coverage Area Re-Assignment Strategy for Multi-Robot Long-Term Surveillance

Uma Heurística para o Problema de Patrulhamento de Ambientes com múltiplos Agentes

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