Hierarchical planning for multiple AGVs in warehouse based on global vision

Yang, Qifan; Lian, Yindong; Xie, Wei

doi:10.1016/j.simpat.2020.102124

Cited by 31 publications

(14 citation statements)

References 12 publications

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“…While Chui et al [68] and Wang et al [69] proposing an improve A* algorithm based on time-window to solve conflictfree in path planning problem for AGV. Next, Yang et al [70] and Ballamajalu et al [71] simulated the A* search method by applying in real warehouse application for materials handling. While, Chen et al [72] proposing a two-stage congestion-aware routing strategy based on A* algorithm.…”

Section: ) A-starmentioning

confidence: 99%

“…To find the lowest-cost path in a geometric graph Proposed algorithm successful selfconfiguring based on the graph and parameters such as turning costs Zheng et al [66] Improved A* To quickly find the optimal path for AGV Success to search the optimal path and path search speed is faster than traditional A* Zhang et al [67] Improved A* To optimize the motion path, reduction of path length, number of AGV turns and path planning time Success to provide efficient path planning with shorter routes, less turn times and shorter operation time compared with traditional A* algorithm and ACO Cui et al [68] Improved A* To solve the conflict-free in AGV path planning problem Success to speed up the path searching process Li et al [69] Improved A* To eliminate the limitation of node movement direction in traditional A* algorithm Success to simulate the working security and efficiency of mobile robot compared to traditional A* Yang et al [70] Improved A* To avoid collisions and search the idle path Success to simulated effectively schedules in the warehouse with lower time complexity for multi-AGV Ballamajalu et al [71] A*…”

Section: Zhao Et Al [25]mentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

2023

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Automated Guided Vehicle (AGV) have received a lot of attention in recent years in terms of both hardware and software research. Nowadays, the AGV offer more adaptable and effective industrial and transportation system solutions. An AGV navigation techniques is essential to its operation. The decision to use AGV navigation is not simple, though to be suite to the application and sufficient. This paper survey the navigation approaches applied in AGV in the past five years of published academic research. In doing so, this work responds to three related questions: (1) is the AGV classical navigation techniques still relevant to the current application area; (2) is the heuristic navigation techniques itself able to optimize AGV movement in term of guide and strategy; (3) is the use of Artificial Intelligence (AI) in AGV navigation techniques able to increase system performance. As a result, numerous techniques for the AGV navigation have been developed globally. On the other hand, the most popular navigation approaches are provided below for more research.

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Section: ) A-starmentioning

confidence: 99%

Section: Zhao Et Al [25]mentioning

confidence: 99%

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

2023

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“…Li et al [9] proposed a conflictfree routing strategy to improve the system efficiency with the loop avoidance capability and designed a dynamic scheduling strategy to find the appropriate AGV to perform the task to reduce the total traveling distance and waiting time. Yang et al [10] proposed a global vision-based hierarchical planning algorithm that adds road congestion to the evaluation metrics and combines A* and time window algorithms to search for idle paths and avoid collisions. In dynamic environments, the computation of time windows is affected by many factors, such as acceleration and deceleration times of AGVs and external obstacles, making it tough to calculate time windows accurately.…”

Section: • Unequal Distance Between Identification Pointsmentioning

confidence: 99%

Dynamic Spare Point Application Based Coordination Strategy for Multi-AGV Systems in a WIP Warehouse Environment

Feng

Liu

et al. 2022

IEEE Access

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Multi Automated Guided Vehicle (multi-AGV) systems are widely used in Work-in-Process (WIP) warehouses to improve the efficiency of material transportation. However, collisions and deadlocks between AGVs are inevitable. Many algorithms have been proposed to solve these problems, but their performance is inefficient in the WIP warehouse due to the lack of consideration of its features. In this paper, to fill the gap between current research and real-world application requirement, we construct a collision and deadlock solving model for a multi-AGV system in a WIP warehouse (CDSMWW). Then we propose a coordination strategy based on dynamic spare point application (DSPA), aiming to improve the efficiency of solving AGV collisions and deadlocks in a WIP warehouse. In this method, the AGV records its predecessor, successor, and reserved points in its local controller and can only enter the set of sequential shared points if its application for spare points is successful. Otherwise, it has to stop to avoid causing an unresolvable deadlock. If a deadlock occurs while this AGV is in the sequential shared points, the AGV moves to the spare point to resolve it. Whenever an AGV leaves the set of consecutive shared points, it releases all its spare points. We apply the algorithm to both the central and local controllers to improve the calculation efficiency. Extensive simulation results demonstrate the feasibility of the DSPA scheme under realistic WIP warehouse environment and its higher efficiency compared to previous state-of-the-art methods.

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“…Since obstacles are usually irregular in shape, if a grid contains any obstacle area, the entire grid is considered as an obstacle grid. Dynamic obstacles can move randomly at the vertices of graph G. We consider a similar scenario in [33], where a camera is installed at the top of the AGV workspace to capture dynamic obstacle information. This paper assumes that the AGV can obtain its global position in the environment in the process of AGV movement.…”

Section: Problem Descriptionmentioning

confidence: 99%

Improved Dyna-Q: A Reinforcement Learning Method Focused via Heuristic Graph for AGV Path Planning in Dynamic Environments

Liu

Yan

Zhao

et al. 2022

Drones

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Dyna-Q is a reinforcement learning method widely used in AGV path planning. However, in large complex dynamic environments, due to the sparse reward function of Dyna-Q and the large searching space, this method has the problems of low search efficiency, slow convergence speed, and even inability to converge, which seriously reduces the performance and practicability of it. To solve these problems, this paper proposes an Improved Dyna-Q algorithm for AGV path planning in large complex dynamic environments. First, to solve the problem of the large search space, this paper proposes a global path guidance mechanism based on heuristic graph, which can effectively reduce the path search space and, thus, improve the efficiency of obtaining the optimal path. Second, to solve the problem of the sparse reward function in Dyna-Q, this paper proposes a novel dynamic reward function and an action selection method based on the heuristic graph, which can provide more intensive feedback and more efficient action decision for AGV path planning, effectively improving the convergence of the algorithm. We evaluated our approach in scenarios with static obstacles and dynamic obstacles. The experimental results show that the proposed algorithm can obtain better paths more efficiently than other reinforcement-learning-based methods including the classical Q-Learning and the Dyna-Q algorithms.

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Hierarchical planning for multiple AGVs in warehouse based on global vision

Cited by 31 publications

References 12 publications

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

Dynamic Spare Point Application Based Coordination Strategy for Multi-AGV Systems in a WIP Warehouse Environment

Improved Dyna-Q: A Reinforcement Learning Method Focused via Heuristic Graph for AGV Path Planning in Dynamic Environments

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