Development of Path Planning Approach Based on Improved A-star Algorithm in AGV System

Zhang, Yan; Lin, Hsiung-Cheng; Ma, Zewen; Zhao, Jiang

doi:10.1007/978-3-030-00410-1_32

Cited by 29 publications

(23 citation statements)

References 1 publication

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“…It is a global search algorithm commonly used in the AUV path planning. However, the A * algorithm will generate some unnecessary inflection points in the search process [31]. Moreover, the resulting final path is a multisegment path composed of many continuous straight lines, which is difficult to meet the requirements of AUV dynamic and kinematic constraints.…”

Section: 22mentioning

confidence: 99%

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Guo

Liu

Fan

et al. 2021

Mathematical Problems in Engineering

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Path planning is a key technology for autonomous underwater vehicle (AUV) navigation. With the emphasis and research on AUV, AUV path planning technology is continuously developing. Path planning techniques generally include environment modelling methods and path planning algorithms. Based on a brief description of the environment modelling methods, this paper focuses on the path planning algorithms commonly used by AUV. According to the basic principles of the algorithm, the AUV path planning algorithms are divided into four categories: artificial potential field methods, geometric model search methods, random sampling methods, and intelligent bionic methods. In this review, we summarize in detail the development and application of various path planning algorithms in recent years. Meanwhile, we analyse the advantages and disadvantages of various algorithms and their improvement methods. Obstacles, ocean currents, and undersea terrain have an impact on AUV path planning. Therefore, how to deal with the complex underwater environment adds some limits to AUV path planning algorithms. In addition to the external environment, path planning algorithms also need to consider AUV’s physical constraints, such as energy constraints and motion constraints. Then, we analyse the motion constraints in AUV path planning. Finally, we discuss the development direction of AUV path planning algorithm. Time-varying ocean currents, special obstacles, multiobjective constraints, and practicability will be the problems that AUV path planning algorithms need to solve.

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Section: 22mentioning

confidence: 99%

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Guo

Liu

Fan

et al. 2021

Mathematical Problems in Engineering

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show abstract

“…In addition, compared with traditional algorithms, the algorithm and strategy proposed in this paper have the advantages of high efficiency, strong adaptive ability and low power consumption. For example, compared with the A star algorithm [26], it has the advantages of smooth trajectory, speed stability , low calculation complexity, and real-time dynamic characteristics; compared with the improved APF algorithm [27], it has the advantages of stable trajectory, fast convergence, short time consumption, high calculation accuracy and autonomous learning; compared with GBSOM and SOM [13], it has the advantages of dynamic and flexible grid space, efficient arrangement of neurons, step size can be adaptively changed, and adapt to time-varying ocean currents.…”

Section: Figure1 Flow Chart Of the Full Text Research Processmentioning

confidence: 99%

“…For example, to expand the influence range of the obstacle domain so that the sub-target nodes are far away from the obstacle space, which provides fault tolerance space for the errors caused by ocean current interference and sensor imbalance during the operation of the AUV individuals In addition, we consulted a large number of documents related to this research and discovered a widely used SOM algorithm [13], which has gradually become a research hotspot. However, the SOM algorithm and the improved Astar algorithm [26] have similar drawbacks, that is, there is a risk of speed jumping during the operation. In order to verify the accuracy of the algorithm proposed in this paper, we conducted a comparison simulation experiment between BNWN and SOM/Improved APF.…”

Section: Figure16: Simulation Experiments Comparisonmentioning

confidence: 99%

Multi-AUV Collaborative Operation Based on Time-Varying Navigation Map and Dynamic Grid Model

Chen

Bai

et al. 2020

IEEE Access

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In a dynamic and complex environment, to improve the cooperative operation efficiency of multiple AUV groups, a bionic neural wave network (BNWN) algorithm, and a velocity vector synthesis (VVS) algorithm are proposed. A strategy of space decomposition and node space recursion is adopted to provide dynamic navigation maps for AUV monomers and to modularize the tasks. A closed boundary function is introduced to construct a dynamic grid model to autonomously avoid obstacles with multiple moving forms. The results of three sets of simulation experiments show that the number of changes in direction, the total path length, and the collision rate of AUV individuals are greatly reduced. These results prove that the proposed algorithm has high autonomy and strong adaptability.

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“…Therefore, centralized path planning methods are applicable only to the small-scale or low-density AGVS [29]. As for the UGN, which represents a static map, vehicles can directly use the classic Dijkstra algorithm [30] or A-star algorithm [31] to realize the autonomous path planning function. However, vehicles often conflict with each other when they are moving, so it is necessary to use an effective centralized traffic management approach to prevent collisions between vehicles.…”

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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Development of Path Planning Approach Based on Improved A-star Algorithm in AGV System

Cited by 29 publications

References 1 publication

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Multi-AUV Collaborative Operation Based on Time-Varying Navigation Map and Dynamic Grid Model

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

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