An algorithm for routing heterogeneous vehicles in robotized warehouses

Baras, Nikolaos; Dasygenis, Minas

doi:10.1109/pacet48583.2019.8956244

Cited by 10 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this research, two objective functions were set, one for the deviation of delivery time from an initial position to a destination, and another for the sum of the total traveling time. A different algorithm was proposed to reduce the delivery time to utilize multiple AGV, based on its specifications, i.e., speed, type and current position to tailor the path for each AGV [137]. In addition to the above, many scholars have researched the relationship of the time spent in the system to other factors.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…[26] Li et al first proposed an exact Dubins multi-robot coverage path planning (EDM) algorithm based on mixed linear integer programming (MILP) and a heuristic approximate credit-based Dubins multirobot coverage path planning (CDM) algorithm to deal with the coverage missions in different scenes. [27] Baras and Dasygenis propose an innovative approach that first segments the area into multiple subareas, [28] which are allocated to individual UGVs, then employs fine-tuned spanning trees to minimize the number of turns during navigation, resulting in more efficient and energy-aware coverage paths.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm of Complete Coverage Path Planning for Deep‐Sea Mining Vehicle Clusters Based on Reinforcement Learning

Xing,

Wang,

Liu

2024

Advcd Theory and Sims

View full text Add to dashboard Cite

This paper proposes a deep reinforcement learning algorithm to achieve complete coverage path planning for deep‐sea mining vehicle clusters. First, the mining vehicles and the deep‐sea mining environment are modeled. Then, this paper implements a series of algorithm designs and optimizations based on Deep Q Networks (DQN). The map fusion mechanism can integrate the grid matrix data from multiple mining vehicles to get the state matrix of the complete environment. In this paper, a preprocessing method for the state matrix is also designed to provide suitable training data for the neural network. The reward function and action selection mechanism of the algorithm are also optimized according to the requirements of cluster cooperative operation. Furthermore, the algorithm uses distance constraints to prevent the entanglement of underwater hoses. To improve the training efficiency of the neural network, the algorithm filters and extracts training samples for training through the sample quality score. Considering the requirement of cluster complete coverage mission, this paper introduces Long Short‐Term Memory (LSTM) based on the neural network to achieve a better training effect. After completing the above optimization and design, the algorithm proposed in this paper is verified through simulation experiments.

show abstract

“…However, as warehouses scale in both size and technological complexity, new challenges emerge. Two primary issues dominate current discussions: the efficient navigation and task allocation for UGVs [5][6][7][8] and the strategic placement of power stations and batteries to support their continuous operation [9]. An inappropriately placed power station can create workflow disruptions, resulting in decreased UGV operational times and overall inefficiency.…”

Section: Introductionmentioning

confidence: 99%

Improving the Efficiency of Modern Warehouses Using Smart Battery Placement

Baras,

Chatzisavvas,

Ziouzios

et al. 2023

Future Internet

Self Cite

View full text Add to dashboard Cite

In the ever-evolving landscape of warehousing, the integration of unmanned ground vehicles (UGVs) has profoundly revolutionized operational efficiency. Despite this advancement, a key determinant of UGV productivity remains its energy management and battery placement strategies. While many studies explored optimizing the pathways within warehouses and determining ideal power station locales, there remains a gap in addressing the dynamic needs of energy-efficient UGVs operating in tandem. The current literature largely focuses on static designs, often overlooking the challenges of multi-UGV scenarios. This paper introduces a novel algorithm based on affinity propagation (AP) for smart battery and charging station placement in modern warehouses. The idea of the proposed algorithm is to divide the initial area into multiple sub-areas based on their traffic, and then identify the optimal battery location within each sub-area. A salient feature of this algorithm is its adeptness at determining the most strategic battery station placements, emphasizing uninterrupted operations and minimized downtimes. Through extensive evaluations in a synthesized realistic setting, our results underscore the algorithm’s proficiency in devising enhanced solutions within feasible time constraints, paving the way for more energy-efficient and cohesive UGV-driven warehouse systems.

show abstract

An algorithm for routing heterogeneous vehicles in robotized warehouses

Cited by 10 publications

References 8 publications

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

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

An Algorithm of Complete Coverage Path Planning for Deep‐Sea Mining Vehicle Clusters Based on Reinforcement Learning

Improving the Efficiency of Modern Warehouses Using Smart Battery Placement

Contact Info

Product

Resources

About