Design and implementation of deep neural network-based control for automatic parking maneuver process

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dc.contributor.authorChai, Runqi
dc.contributor.authorTsourdos, Antonios
dc.contributor.authorSavvaris, Al
dc.contributor.authorChai, Senchun
dc.contributor.authorXia, Yuanqing
dc.contributor.authorChen, C. L. Philip
dc.date.accessioned2021-03-12T15:17:48Z
dc.date.available2021-03-12T15:17:48Z
dc.date.issued2020-12-17
dc.description.abstractThis article focuses on the design, test, and validation of a deep neural network (DNN)-based control scheme capable of predicting optimal motion commands for autonomous ground vehicles (AGVs) during the parking maneuver process. The proposed design utilizes a multilayer structure. In the first layer, a desensitized trajectory optimization method is iteratively performed to establish a set of time-optimal parking trajectories with the consideration of noise-perturbed initial configurations. Subsequently, by using the preplanned optimal parking trajectory data set, several DNNs are trained in order to learn the functional relationship between the system state-control actions in the second layer. To obtain further improvements regarding the DNN performances, a simple yet effective data aggregation approach is designed and applied. These trained DNNs are then utilized as the motion controllers to generate feedback actions in real time. Numerical results were executed to demonstrate the effectiveness and the real-time applicability of using the proposed control scheme to plan and steer the AGV parking maneuver. Experimental results were also provided to justify the algorithm performance in real-world implementations.en_UK
dc.identifier.citationChai R, Tsourdos A, Savvaris A, et al., (2022) Design and implementation of deep neural network-based control for automatic parking maneuver process. IEEE Transactions on Neural Networks and Learning Systems, Volume 33, Issue 4, April 2022, pp. 1400-1412en_UK
dc.identifier.issn2162-237X
dc.identifier.urihttps://doi.org/10.1109/TNNLS.2020.3042120
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16474
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjecttrajectory optimizationen_UK
dc.subjectparking maneuveren_UK
dc.subjectmotion controlleren_UK
dc.subjectdeep neural network (DNN)en_UK
dc.subjectAutonomous ground vehicles (AGVs)en_UK
dc.titleDesign and implementation of deep neural network-based control for automatic parking maneuver processen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2020-11-25

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