A dynamic path planning approach for dense, large, grid-based automated guided vehicle systems

Fransen, K. J. C.; Eekelen, J.A.W.M. van; Pogromsky, Alexander Yu.; Boon, Marko; Adan, I.J.B.F.

doi:10.1016/j.cor.2020.105046

Cited by 48 publications

(21 citation statements)

References 21 publications

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“…(11) Ensure that only one picking stations is responsible for each task; (12) represents the constraint of the starting time of continuous tasks before and after a single picking platform; (13) and (14) indicate that the election platform can only execute one task at most before and after each task, to ensure the relationship between tasks before and after the election platform. (15) represents the constraint between the time when AGV starts to execute the task and the time when the picking station starts to execute the task under the same task; (16) represents the relationship between the time when the picking stage begins to execute the task under the same task and the time when the AGVs end the task; (17) shows the value range of the variable.…”

Section: B Equipment-task Scheduling Modelmentioning

confidence: 99%

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Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

et al. 2021

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To improve the throughput performance of the autonomous guide vehicle (AGV) unmanned storage system, a two-stage mathematical model was established. The model also considers the equipment configuration of the AGV unmanned storage system and the AGV-picking stations dual resource coordination scheduling problem. In the equipment-task scheduling phase, the model aims at the shortest order completion time, while the equipment configuration and layout model aims at the minimum equipment configuration and operating cost. To solve the two-stage model, a two-layer genetic algorithm was designed. The inner layer algorithm was used to optimize the task scheduling order of AGVs and picking stations. The results of the inner layer algorithm are fed back to the outer model to optimize configuration of the equipment and the picking station's layout. The inner and outer loops are combined to obtain the optimal equipment configuration scheme. Through the simulation study of an enterprise AGV unmanned storage case, the optimal equipment configuration combination and picking stations layout scheme are obtained. Compared with the equipment configuration scheme based on the principle the task scheduling in operation is another key link that affects the picking efficiency of an unmanned warehouse of random task scheduling and the principle of shortest job time first; The model can improve the efficiency of warehouse retrieval and minimize the number of equipment configurations. Finally, the improved genetic algorithm is used to solve the model, and the performance is compared with that of LINGO to verify the effectiveness of the improved algorithm.

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Section: B Equipment-task Scheduling Modelmentioning

confidence: 99%

“…Zhong et al [14] studied the integrated scheduling of the conflict-free path planning of multiple AGVs, and solved the problem by using the hybrid genetic algorithmparticle swarm optimization; K.J.C. Fransen [15] consider the real-time path planning for large, dense grid-based automated guided vehicle systems with congestion.…”

Section: Introductionmentioning

confidence: 99%

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

et al. 2021

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“…During the path generation process, each robot calculates its next local optimal coordinates in a stepwise manner to avoid path conflicts of multiple AGVs. Fransen et al [23] conduct research on largescale multi-AGVs systems, and update the weights of road segment length according to the waiting time of the AGV when the paths of multiple AGVs conflict. Lyu et al [24] embed the Dijkstra algorithm into the genetic algorithm to search for the shortest path, and uses the time window to detect the path conflicts of multiple vehicles, and finally obtain the shortest conflict-free paths of multiple AGVs.…”

Section: Related Workmentioning

confidence: 99%

Research on Multi-AGVs Path Planning and Coordination Mechanism

et al. 2020

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Reasonable automatic guided vehicle path planning can shorten the transportation time of materials and improve the production efficiency of the intelligent assembly workshop. Ant colony algorithm is a widely used path planning method, however, it suffers from the shortcomings that being easy to fall into local optimum and low search efficiency. To overcome these shortcomings, first, this paper proposes a step optimization method to improve the search efficiency of the ant colony algorithm, and a path simplification method to avoid getting blindly tortuous paths; Second, to overcome the problem that the ant colony algorithm is easy to fall into the local optimum, this paper proposes an adaptive pheromone volatilization coefficient strategy, which uses different pheromone volatilization coefficients at different stages of the search path; third, for the path conflict problem of multiple automatic guided vehicles, this paper proposes a load balancing strategy to avoid it, which is based on the consideration that, path conflicts are caused by excessive concentration of multiple automatic guided vehicles paths. Extensive simulation results demonstrate the feasibility and efficiency of the proposed methods. INDEX TERMS ant colony algorithm, intelligent assembly, multiple automatic guided vehicles, path planning;

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“…The preliminary layout tasks is to map the background environment towards a geographical representation that integrates the features and shapes. Various algorithms have been so far proposed to model the environment such as Voronoi diagrams [9], visibility graphs [10] and grid structures [11].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Xie et al [15] transformed an offshore wind farm into a grid map. Fransen et al [16] derived a parcel sorting layout, the bag handling layouts at an airport, and a semiconductor fabrication layout bay through the grid method. Tsatcha et al [17] used a series of hexagonal grids to simulate sea lanes.…”

Section: Introductionmentioning

confidence: 99%

Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment

et al. 2021

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This research introduces a path planning method based on the geometric A-star algorithm.The whole approach is applied to an Automated Guided Vehicle (AGV) in order to avoid the problems of many nodes, long-distance and large turning angle, and these problems usually exist in the sawtooth and cross paths produced by the traditional A-star algorithm. First, a grid method models a port environment. Second, the nodes in the close-list are filtered by the functions ( ) , P x y and ( ) , W x y and the nodes that do not meet the requirements are removed to avoid the generation of irregular paths. Simultaneously, to enhance the stability of the AGV regarding turning paths, the polyline at the turning path is replaced by a cubic B-spline curve. The path planning experimental results applied to different scenarios and different specifications showed that compared with other seven different algorithms, the geometric A-star algorithm reduces the number of nodes by 10% ~ 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5%. Overall, the simulation results of the path planning confirmed the effectiveness of the geometric A-star algorithm.

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A dynamic path planning approach for dense, large, grid-based automated guided vehicle systems

Cited by 48 publications

References 21 publications

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

Research on Multi-AGVs Path Planning and Coordination Mechanism

Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment

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