Efficient and near-optimal global path planning for AGVs: a DNN-based double closed-loop approach with guarantee mechanism

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dc.contributor.authorZhang, Runda
dc.contributor.authorChai, Runqi
dc.contributor.authorChen, Kaiyuan
dc.contributor.authorZhang, Jinning
dc.contributor.authorChai, Senchun
dc.contributor.authorXia, Yuanqing
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2024-09-18T15:15:47Z
dc.date.available2024-09-18T15:15:47Z
dc.date.freetoread2024-09-18
dc.date.issued2024
dc.date.pubOnline2024-06-25
dc.description.abstractIn this article, a novel global path planning approach with rapid convergence properties for autonomous ground vehicles (AGVs) named neural sampling rapidly exploring random tree (NS-RRT*) is proposed. This approach has a three-layer structure to obtain a feasible and near-optimal path. The first layer is the data collection stage. Utilizing the target area adaptive rapidly exploring random tree (TAA-RRT*) algorithm to establish a collection of paths considering the initial noise disturbance. To enhance network generalization, an optimal path backward generation (OPBG) strategy is introduced to augment the dataset size. In the second layer, the deep neural network (DNN) is trained to learn the relationships between the states and the sampling strategies. In the third layer, the trained model is used to guide RRT* sampling, and an efficient guarantee mechanism is also designed to ensure the feasibility of the planning task. The proposed algorithm can assist the RRT* algorithm in efficiently obtaining optimal or near-optimal strategies, significantly enhancing search efficiency. Numerical results and experiments are executed to demonstrate the feasibility and efficiency of the proposed method.
dc.description.journalNameIEEE Transactions on Industrial Electronics
dc.identifier.citationZhang R, Chai R, Chen K, et al., (2024) Efficient and near-optimal global path planning for AGVs: a DNN-based double closed-loop approach with guarantee mechanism. IEEE Transactions on Industrial Electronics, Available online 25 June 2024
dc.identifier.eissn1557-9948
dc.identifier.elementsID547139
dc.identifier.issn0278-0046
dc.identifier.issueNo99
dc.identifier.urihttps://doi.org/10.1109/tie.2024.3409883
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22954
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.urihttps://ieeexplore.ieee.org/document/10569009
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectPath planning
dc.subjectPlanning
dc.subjectArtificial neural networks
dc.subjectLand vehicles
dc.subjectCosts
dc.subjectTrajectory
dc.subjectProbabilistic logic
dc.subjectAutonomous ground vehicles (AGVs)
dc.subjectdeep neural network (DNN)
dc.subjectpath planning
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subjectElectrical & Electronic Engineering
dc.subject40 Engineering
dc.subject46 Information and computing sciences
dc.titleEfficient and near-optimal global path planning for AGVs: a DNN-based double closed-loop approach with guarantee mechanism
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-05-27

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