Task Selection by Autonomous Mobile Robots in A Warehouse Using Deep Reinforcement Learning

Li, Maojia P.; Sankaran, Prashant; Kühl, Michael; Ptucha, Raymond; Ganguly, Amlan; Kwasinski, Alexis

doi:10.1109/wsc40007.2019.9004792

Cited by 20 publications

(14 citation statements)

References 18 publications

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“…In order to control the cost of charging pile, the method of time allocation is adopted. When the robot is charging at the charging pile i s , the calculation formula of the charging state value i s q is as formula (1)…”

Section: B Charging Pile State Querymentioning

confidence: 99%

“…1. When a robot is charging at the charging pile, the state value of the charging pile is calculated by formula (1). When no robot is charging at the charging pile or the robot leaves the charging pile after charging, the value of the charging state of the charging pile is 0.…”

Section: Figure 1 Flow Diagram Of Updating and Sending Charging State...mentioning

confidence: 99%

“…Service robots usually work in an indoor environment with multiple charging piles, such as the handling robot [1] in the warehouse, the disinfection robot [2] in the hospital, the sales robot [3] in the shopping mall, and the patrol robot [4] in the airport. They use various path planning algorithms for navigation.…”

Section: Introductionmentioning

confidence: 99%

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Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

Deng

Xie

et al. 2023

IEEE Access

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Autonomous mobile service robots are used to complete many tasks, such as cleaning, transporting goods and monitoring. Such tasks usually require uninterrupted and continuous service. However, the battery of the robot is limited and must be charged frequently. For a large number of robots, it is essential to select a suitable charging pile. For this issue, we propose a path planning model for robots to intelligently access a limited number of charging piles distributed on the map. The traditional path planning model mainly considers the shortest path criterion to generate the path. Different from this, the path planning model in this paper not only considers the shortest path, but also the service position of the robot after charging, the remaining power of the robot, the state of the charging pile and the position of the robot in the map. Our path planning assigns the most suitable charging pile to the robot that needs charging. To solve the problem of high memory consumption and slow search speed when traditional A* algorithm is used for path planning, we propose local memorial path planning (LMPP) algorithm to quickly generate effective paths. The simulation results show that the proposed robot charging path planner can improve the robot service satisfaction and plan the effective path to the available charging piles.INDEX TERMS Autonomous mobile robot, service robot, path planning, robot charging

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Section: B Charging Pile State Querymentioning

confidence: 99%

Section: Figure 1 Flow Diagram Of Updating and Sending Charging State...mentioning

confidence: 99%

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Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

Deng

Xie

et al. 2023

IEEE Access

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“…Tasks were evaluated by three attributes and the tasks with the greatest evaluation value would be served by the AGV. A model was presented based on deep Q network training (DQN) to assign the task with the departure point closest to the idle AGV, and the least vehicles in the path to improve the completion time and avoid conflicts during operation [41]. The neural network method needs to be retrained when the environment changes.…”

Section: Multi-attribute Strategies a Vehicle Initiated Task Assignme...mentioning

confidence: 99%

“…Ventura et al [24] optimized the completion time by improving the parking position of idle AGVs and minimizing the maximum response time. The deep Q-NET method was used to shorten the average time of the task and minimize the completion time [41]. An effective evolutionary approach was used to minimize completion time and the number of AGVs at the same time [22].…”

Section: The Shortest Time To Completementioning

confidence: 99%

Analysis of Multi-AGVs Management System and Key Issues: A Review

Lu¹,

Guo²,

Song³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Multiple Automatic Guided Vehicle (multi-AGVs) management systems provide an effective solution to ensuring stable operations of multi-AGVs in the same scenario, such as flexible manufacturing systems, warehouses, container terminals, etc. This type of systems need to balance the relationship among the resources of the system and solve the problems existing in the operation to make the system in line with the requirement of the administrator. The multi-AGVs management problem is a multi-objective, multi-constraint combinatorial optimization problem, which depends on the types of application scenarios. This article classifies and compares the research papers on multi-AGVs management in detail. Firstly, according to the different dimensions of the problem, the multi-AGVs management system is analyzed from three perspectives, namely, 1) task dimensiondispatch, 2) spatial dimension-path, and 3) time dimension-scheduling. The detailed comparison between the three dimensions and their respective solutions are discussed in detail as well. Secondly, according to their utility, the multi-AGVs management problems are divided into three categories: 1) cost reduction, resource-oriented, 2) efficiency improvement, problem-oriented, 3) personalized demand, goal-oriented. The algorithm and methods of the different utility-oriented are analyzed and discussed. The related literature is summarized and corresponds to the composition of the multi-AGVs management system and the multi-AGVs management problems. Finally, according to the literature review, suggestions are made for further research.

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