Research on robot charging strategy based on the scheduling algorithm of minimum encounter time

Cheng, Zongmao; Fu, Xiao; Wang, Jing; Xu, Xianghua

doi:10.1080/01605682.2019.1654941

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The adoption of mobile chargers improves the flexibility of the charging process. Cheng et al (2021) study the minimum required quantity of mobile chargers and the scheduling of mobile chargers for power supplements of robots.…”

Section: Strategic Decisionmentioning

confidence: 99%

A literature review of smart warehouse operations management

Zhen

2022

Front. Eng. Manag.

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E-commerce, new retail, and other changes have highlighted the requirement of high efficiency and accuracy in the logistics service. As an important section in logistics and supply chain management, warehouses need to respond positively to the increasing requirement. The “smart warehouse” system, which is equipped with emerging warehousing technologies, is increasingly attracting the attention of industry and technology giants as an efficient solution for the future of warehouse development. This study provides a holistic view of operations management problems within the context of smart warehouses. We provide a framework to review smart warehouse operations management based on the characteristics of smart warehouses, including the perspectives of information interconnection, equipment automation, process integration, and environmental sustainability. A comprehensive review of relevant literature is then carried out based on the framework with four perspectives. This study could provide future research directions on smart warehouses for academia and industry practitioners.

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Section: Strategic Decisionmentioning

confidence: 99%

A literature review of smart warehouse operations management

Zhen

2022

Front. Eng. Manag.

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“…Various scheduling techniques employed for mobile robots primarily focused on task-based scheduling. In previous studies, various scheduling problems were solved using integer linear programming (Tiotsop et al, 2020) (Cheng et al, 2019), and dynamic programming (Jin et al, 2006). Optimal scheduling problems arising in different real-world activities have been solved using classical search and optimization algorithms including linear programming methods.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary approach to robot scheduling in protected cultivation systems for uninterrupted and maximization of working time

Uyeh

Pamulapati

Mallipeddi

et al. 2021

Computers and Electronics in Agriculture

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“…Some methods used in designing a scheduling strategy are linear programming, nonlinear programming, clustering analysis, TSP algorithm, queuing theory, and other mathematical methods. In this way, Cheng et al (2021) proposed a mesh model to cope with the robot’s limited movements and the MCs. Considering the distances between the MCs and the robot and the expended time at the charge station, they developed a scheduling algorithm of minimum encounter time to solve the recharging problem in a robot that executes a priority task.…”

Section: Introductionmentioning

confidence: 99%

Reactive navigation under a fuzzy rules-based scheme and reinforcement learning for mobile robots

López-Lozada

Rubio-Espino

Azuela

et al. 2021

PeerJ Computer Science

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Robot navigation allows mobile robots to navigate among obstacles without hitting them and reaching the specified goal point. In addition to preventing collisions, it is also essential for mobile robots to sense and maintain an appropriate battery power level at all times to avoid failures and non-fulfillment with their scheduled tasks. Therefore, selecting the proper time to recharge the batteries is crucial to address the navigation algorithm design for the robot’s prolonged autonomous operation. In this paper, a machine learning algorithm is used to ensure the extended robot autonomy based on a reinforcement learning method combined with a fuzzy inference system. The proposal enables a mobile robot to learn whether to continue through its path toward the destination or modify its course on the fly, if necessary, to proceed toward the battery charging station, based on its current state. The proposal performs a flexible behavior to choose an action that allows a robot to move from a starting to a destination point, guaranteeing battery charge availability. This paper shows the obtained results using an approach with thirty-six states and its reduction with twenty states. The conducted simulations show that the robot requires fewer training epochs to achieve ten consecutive successes in the fifteen proposed scenarios than traditional reinforcement learning methods exhibit. Moreover, in four scenarios, the robot ends up with a battery level above 80%, that value is higher than the obtained results with two deterministic methods.

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Research on robot charging strategy based on the scheduling algorithm of minimum encounter time

Cited by 6 publications

References 9 publications

A literature review of smart warehouse operations management

A literature review of smart warehouse operations management

An evolutionary approach to robot scheduling in protected cultivation systems for uninterrupted and maximization of working time

Reactive navigation under a fuzzy rules-based scheme and reinforcement learning for mobile robots

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