Robotic Mobile Fulfillment Systems: A survey on recent developments and research opportunities

Barros, Ítalo Renan da Costa; Nascimento, Tiago P.

doi:10.1016/j.robot.2021.103729

Cited by 61 publications

(28 citation statements)

References 38 publications

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“…In many environments such as warehouses, docks, communities, and office buildings, there exists a large number of parcels to be transported. Such The PD task accomplished via different means [11].…”

Section: A Related Workmentioning

confidence: 99%

Towards Safe and Efficient Swarm-Human Collaboration: A Hierarchical Multi-Agent Pickup and Delivery framework

Gong¹,

Wang²,

Cui³

et al. 2022

Preprint

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The multi-Agent Pickup and Delivery (MAPD) problem is crucial in the realm of Intelligent Storage Systems (ISSs), where multiple robots are assigned with time-varying, heterogeneous, and potentially uncertain tasks. When it comes to Human-Swarm Hybrid System ((HS) 2 ), robots and human workers will accomplish the MAPD tasks in collaboration. Herein, we propose a Human-Swarm Hybrid System Pickup and Delivery ((HS) 2 PD) framework, which is predominant in future ISSs. A two-layer decision framework based on the prediction horizon window is established in light of the unpredictability of human behavior and the dynamic changes of tasks. The first layer is a two-level programming problem to solve the problems of mode assignment and TA. The second layer is devoted to the exact path of each agent via solving mixed-integer programming (MIP) problems. An integrated algorithm for the (HS) 2 PD problem is summarized. The practicality and validity of the above algorithm are illustrated via a numerical simulation example towards (HS) 2 PD tasks.

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Section: A Related Workmentioning

confidence: 99%

Towards Safe and Efficient Swarm-Human Collaboration: A Hierarchical Multi-Agent Pickup and Delivery framework

Gong¹,

Wang²,

Cui³

et al. 2022

Preprint

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“…is makes them well suited for the tight deadlines of e-commerce [4,5]. For relevant research about RMFS, the reader is referred to the survey work of da Costa Barros and Nascimento [6].…”

Section: Introductionmentioning

confidence: 99%

“…Order picking is the process of retrieving products from storage or buffer areas in a warehouse to fulfill the customer orders, typically, in a strict time window. Many factors must be taken into consideration to improve the picking efficiency, such as storage assignment, order batching, and picker routing [6,8,9]. A storage assignment method is the rules which can be used to assign products to storage locations.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Joint Impact of the Storage Assignment and Order Batching in Mobile-Pod Warehouse Systems

Yang

2022

Mathematical Problems in Engineering

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The rapid development of e-commerce has impacted the intelligence level of warehouses. The robotic mobile fulfillment system (RMFS) demonstrated that mobile-pod warehouse systems could process a significant number of order-picking operations within the same amount of time as other systems, saving money and improving operational efficiency. The order-picking mode in such a system is “parts-to-picker,” which is becoming both a prevailing operation mode in industrial warehouses and an encouraging research field during these years. One of the most significant fundamental factors in RMFS is the operational efficiency that is affected by storage assignment and order batching. This paper considers the joint impact of the storage assignment policies and order batching policies on order picking process. Our goal is to minimize the moving times of robots, which reflects the order-picking cost or efficiency. We propose using order similarity and item similarity to batch orders and assign item storage locations, respectively. Both the order batching and item grouping are tackled by a clustering model which is an integer linear program. We also develop a policy evaluation model to measure the order picking cost. We conduct numerical tests on six order batching and storage assignment policy combinations. A comparative analysis and an ANOVA analysis are then performed on the test results to compare the performances of these policy combinations. We find that the Weighted Support Count-based storage allocation combining with the correlation-based order batching achieves the best order-picking performance. Also, the more accurate information about the items and orders we can get from the historical data, the more order-picking workload we can save by exploiting the similarity features.

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“…Path planning is an inevitable issue for autonomous mobile robots which makes collision‐free trajectories for robots to travel from initial position to goal position without any human intervention in various environments where both static and dynamic obstacles can be present (Erke et al, 2020). Therefore, it draws growing attention from researchers over the last two decades (da Costa Barros & Nascimento, 2021; Lavalle, 2006). It depends on precisely sense static or moving obstacles and avoid them during the movement of robots.…”

Section: Introductionmentioning

confidence: 99%

“…Real‐time obstacle detection and avoidance system deal with different challenging situations in real environmental scenarios. There are two types of path planning strategies depending on the static and dynamic environments, one of them is a global path planning strategy and the other one is a local or reactive path planning strategy (da Costa Barros & Nascimento, 2021; Dolgov et al, 2009). The global path planning strategy uses prior information, where a map, a goal position, and stationary obstacles positions are known to the robot to generate a collision‐free trajectory.…”

Section: Introductionmentioning

confidence: 99%

Local path planning for autonomous mobile robots by integrating modified dynamic‐window approach and improved follow the gap method

Hossain

Habibullah

Islam

et al. 2021

Journal of Field Robotics

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Mobile robots need to automatically generate a safe, goal‐oriented, and fast collision‐free trajectory in real‐time during the movement in an indoor/outdoor environment. A planned trajectory must be adaptable and drivable with environmental changes where various static and moving obstacles may be present. The ultimate goal of a robot is to reach the destination without hitting any obstacles, therefore, a reactive local path planning algorithm is needed. In this paper, a novel local algorithm is proposed by integrating dynamic window approach (DWA) and improved follow the gap method (IFGM) to generate a collision‐free trajectory for a mobile robot which is capable to avoid any moving obstacles presenting in the surrounding environment. In this proposed method, first, a safety distance is maintained according to the relative position of obstacles and the robot. Moreover, find a feasible gap to direct the robot toward the desired goal. Besides, the heading angle is calculated to change the direction of the robot for avoiding collision with nearby obstacles. After that, calculate the appropriate velocity for the robot. Finally, a robust, safe, and goal‐directed trajectory is generated which does not suffer from global convergence and local minima problems. The performance and effectiveness of this proposed algorithm are evaluated by experimental results.

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Robotic Mobile Fulfillment Systems: A survey on recent developments and research opportunities

Cited by 61 publications

References 38 publications

Towards Safe and Efficient Swarm-Human Collaboration: A Hierarchical Multi-Agent Pickup and Delivery framework

Towards Safe and Efficient Swarm-Human Collaboration: A Hierarchical Multi-Agent Pickup and Delivery framework

Evaluation of the Joint Impact of the Storage Assignment and Order Batching in Mobile-Pod Warehouse Systems

Local path planning for autonomous mobile robots by integrating modified dynamic‐window approach and improved follow the gap method

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