Multi-AGV scheduling for conflict-free path planning in automated container terminals

Zhong, Meisu; Yang, Yongsheng; Dessouky, Yasser; Postolache, Octavian

doi:10.1016/j.cie.2020.106371

Cited by 185 publications

(80 citation statements)

References 39 publications

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“…(5) If the motion satisfies the kinematic constraint Equation (8) and there is no collision in this movement, Add X new to the tree G, and the edge goes from X near to X new ; (6) If X new is in X goal , it ends successfully, if not, it skips to continue to execute the Extend() function.…”

Section: Rrt Algorithm With Kinematic Constraintsmentioning

confidence: 99%

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A Bi-Level Path Planning Algorithm for Multi-AGV Routing Problem

Yuan

Yang

et al. 2020

Electronics

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Avoiding the multi-automated guided vehicle (AGV) path conflicts is of importance for the efficiency of the AGV system, and we propose a bi-level path planning algorithm to optimize the routing of multi-AGVs. In the first level, we propose an improved A* algorithm to plan the AGV global path in the global topology map, which aims to make the path shortest and reduce the AGV path conflicts as much as possible. In the second level, we present the dynamic rapidly-exploring random trees (RRT) algorithm with kinematic constraints to obtain the passable local path with collisions in the local grid map. Compared to the Dijkstra algorithm and classic A* algorithm, the simulation results showed that the proposed bi-level path planning algorithm performed well in terms of the search efficiency, significantly reducing the incidence of multiple AGV path conflicts.

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Section: Rrt Algorithm With Kinematic Constraintsmentioning

confidence: 99%

“…The automated guided vehicle (AGV) plays a vital role in significantly reducing transportation costs and improving transportation efficiency [3,4]. Recent papers focused on planning the paths of AGVs to complete transportation tasks more efficiently in the factory without collision, deadlock, and other traffic problems [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Bi-Level Path Planning Algorithm for Multi-AGV Routing Problem

Yuan

Yang

et al. 2020

Electronics

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“…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. The decoupled approach first computes separate paths for the individual robots and then employs different strategies to resolve possible conflicts.…”

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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“…As a flexible material handling system, the Automated Guided Vehicle System (AGVS) has been widely used in many fields, such as storage and distribution centers, manufacturing workshops, and port transportation [1]. In the control process of the AGVS, many NP-hard problems need to be solved, including task scheduling and vehicle dispatching [2], path planning [3][4][5], and collision and deadlock prevention [6,7]. Among them, the collision prevention is one of the main challenges in the AGVS field.…”

Section: Introductionmentioning

confidence: 99%

A Collision and Deadlock Prevention Method With Traffic Sequence Optimization Strategy for UGN-Based AGVS

Xiao

Qin

et al. 2020

IEEE Access

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Due to the advantages of high efficiency and flexibility, the Automatic Guided Vehicle System (AGVS) has been more and more widely used in many industries. However, one of the main challenges in AGVS control is how to prevent collision and deadlock between vehicles. Although many collision and deadlock prevention algorithms have been proposed, they are inefficient for the AGVS based on a unidirectional guide-path network (UGN). In order to solve this problem, this paper proposes a collision and deadlock prevention method with a traffic sequence optimization strategy for the UGN-based AGVS. First, a vehicle coordination method based on the semaphore theory and Internet of Things (IoT) positioning technology is proposed to prevent collisions and intersection congestion deadlocks. Then, to avoid cycle deadlocks, a cycle deadlock search and avoid algorithm based on the digraph theory is developed. After that a bidding mechanism-based strategy is developed to optimize the vehicle traffic sequence in each path intersection. Finally, extensive simulation tests are conducted to verify the performance of the proposed methods and strategy. Simulation results show that, compared to the zone controlled (ZC) methods, the average travel time of the proposed methods is reduced by 6.2%-29.6%, and the average throughput is increased by 3.5%-27.4%. Also, the bidding mechanism-based traffic sequence optimization strategy can not only increase the average throughput of the processing subsystem but also reduce the congestion and deadlock risk of the logistics transportation subsystem. The proposed method is suitable for an UGN-based AGVS in the manufacturing application environment. INDEX TERMS Automatic guided vehicle system, collision and deadlock prevention, traffic sequence optimization, unidirectional guide-path network.

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Multi-AGV scheduling for conflict-free path planning in automated container terminals

Cited by 185 publications

References 39 publications

A Bi-Level Path Planning Algorithm for Multi-AGV Routing Problem

A Bi-Level Path Planning Algorithm for Multi-AGV Routing Problem

Research on Multi-AGVs Path Planning and Coordination Mechanism

A Collision and Deadlock Prevention Method With Traffic Sequence Optimization Strategy for UGN-Based AGVS

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