Conflict-free route planning of automated guided vehicles based on conflict classification

Zhang, Zheng; Guo, Qing; Yuan, Pingzhi

doi:10.1109/smc.2017.8122819

Cited by 15 publications

(12 citation statements)

References 9 publications

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“…Thus, classical A* algorithm may not be suitable for path planning applications in complex environments such as a future FoF environment that may have changing layouts and dynamic arrangements. Dijkstra algorithm was used for AGVs path planning in [96], while improved Dijkstra algorithm was applied to path planning in [97] and [98]. Specifically, Dijkstra algorithm was used to resolve AGV conflict in [98] where authors classified conflicts that AGVs may experience as cross conflict, path-occupancy conflict and head-on conflict.…”

Section: A Localization Scheduling and Path Planning Algorithmsmentioning

confidence: 99%

“…Dijkstra algorithm was used for AGVs path planning in [96], while improved Dijkstra algorithm was applied to path planning in [97] and [98]. Specifically, Dijkstra algorithm was used to resolve AGV conflict in [98] where authors classified conflicts that AGVs may experience as cross conflict, path-occupancy conflict and head-on conflict. Cross conflict can occur when a single path node falls into the navigation path of two AGVs.…”

Section: A Localization Scheduling and Path Planning Algorithmsmentioning

confidence: 99%

“…Path-occupancy conflict can occur when an AGV travels on a path that is included in the route of another AGV while head-on conflict can occur when two AGVs travel on the same path, but in opposite directions. To implement the improved Dijkstra algorithm developed in [98], the factory environment considered was described using an adjacency matrix with a 2D coordinate using mySQL. Conflict-free routing paths was used to populate the Dijkstra algorithm matrix.…”

Section: A Localization Scheduling and Path Planning Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

et al. 2020

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In industrial environments, over several decades, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have served to improve efficiencies of intralogistics and material handling tasks. However, for system integrators, the choice and effective deployment of improved, suitable and reliable communication and control technologies for these unmanned vehicles remains a very challenging task. Specifics of communication for AGVs and AMRs imposes stringent performance requirements on latency and reliability of communication links which many existing wireless technologies struggle to satisfy. In this paper, a review of latest AGVs and AMRs research results in the past decade is presented. The review encompasses results from different past and present research domains of AGVs. In addition, performance requirements of communication networks in terms of their latencies and reliabilities when they are deployed for AGVs and AMRs coordination, control and fleet management in smart manufacturing environments are discussed. Integration challenges and limitations of present state-of-the-art AGV and AMR technologies when those technologies are used for facilitating AGV-based smart manufacturing and factory of the future applications are also thoroughly discussed. The paper also present a thorough discussion of areas in need of further research regarding the application of 5G networks for AGVs and AMRs fleet management in smart manufacturing environments. In addition, novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.INDEX TERMS Intelligent factory, factory of the future, 5G, smart manufacturing, industry 4.0, autonomous industrial equipment, AGV, AMR, tactile Internet, virtual reality, lean manufacturing.

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Section: A Localization Scheduling and Path Planning Algorithmsmentioning

confidence: 99%

Section: A Localization Scheduling and Path Planning Algorithmsmentioning

confidence: 99%

Section: A Localization Scheduling and Path Planning Algorithmsmentioning

confidence: 99%

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A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

et al. 2020

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“…Demesure et al 22 proposed a navigation scheme based on predetermined path planning and priority, avoiding conflicts when AGVs approach each other or moving toward the same target. Zhang et al 23 proposed a conflict-free path planning method for the multi-AGV system in the warehouse system. Candidate routes are planned by the improved Dijkstra algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on global optimization method for multiple AGV collision avoidance in hybrid path

Cao

Meng

2021

Optim Control Appl Methods

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Due to the increasing number of automated guided vehicles (AGVs) in the multi-AGV system and the limitation of working environment, path conflicts often occur in the working process of AGVs, which affects the working efficiency of the multi-AGV system. Thus, a optimization method by arranging the AGVs' traffic sequence is proposed in this paper. First, an AGV working map is reconstructed with graph theory, and then the corresponding collision avoidance rules are formulated for different types of conflicts. In multi-AGV system, each collision avoidance decision has an impact on the efficiency of the system, so it is crucial to adopt appropriate decisions. To optimize the decisions, the system fitness of different collision avoidance decisions are calculated based on the global state of the system, and the particle swarm optimization (PSO) algorithm is used to optimize the decisions. Furthermore, the PSO algorithm is improved by planning the direction of particle motion in the solution space and introducing mutation operation, so as to improve the search ability of the particle in the solution space. To verify the feasibility and effectiveness of the improved particle swarm optimization (IPSO) algorithm, an experiment system is built based on.NET platform. Results show that the IPSO algorithm than the traditional algorithms experimental performs better. The IPSO algorithm can effectively reduce congestion caused by path conflict and enhance the efficiency of the multi-AGV system.

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“…Research on conflict-free path planning is still on-going and time-window based method is commonly used for multi-AGV conflict-free path planning [3] - [10]. In [4], Zhang and Yuan proposed to find all the equidistance shortest paths in a rectangular environment map by the improved Dijkstra algorithm (as proposed in [3]) and then the path which does not conflict with other planned AGV paths will be selected. In [5], Yan et al proposed some rules and online control strategies to avoid AGV collision and deadlock with both unidirectional and bidirectional paths.…”

Section: Introductionmentioning

confidence: 99%

Conflict-Free Path Planning of Agv Based on Improved a-Star Algorithm

Cui¹,

Ma²,

Fang³

et al. 2019

Recent Developments on Information and Communication Technology (ICT) Engineering

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This paper proposes an improved A* algorithm based on time-window to solve the conflict-free path planning problem for Automated Guided Vehicles (AGVs). In particular, a more precise estimate of the turning time is introduced so that a more accurate prediction of node conflict can be achieved during the path planning stage. Furthermore, a more precise estimation of heuristic value in the A* evaluation function is introduced in which it can further speed up the path searching process as some sub-optimal path candidates will not be checked during the process.

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Conflict-free route planning of automated guided vehicles based on conflict classification

Cited by 15 publications

References 9 publications

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

Research on global optimization method for multiple AGV collision avoidance in hybrid path

Conflict-Free Path Planning of Agv Based on Improved a-Star Algorithm

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