Chai Runqi, Tsourdos Antonios, Savvaris Al, Chai Senchun, Xia Yuanqing, Chen C L Philip
IEEE Trans Neural Netw Learn Syst. 2022 Apr;33(4):1400-1413. doi: 10.1109/TNNLS.2020.3042120. Epub 2022 Apr 4.
This 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.
本文重点关注一种基于深度神经网络（DNN）的控制方案的设计、测试和验证，该方案能够在停车操纵过程中为自主地面车辆（AGV）预测最优运动指令。所提出的设计采用多层结构。在第一层，迭代执行一种脱敏轨迹优化方法，以考虑噪声干扰的初始配置来建立一组时间最优停车轨迹。随后，通过使用预先规划的最优停车轨迹数据集，训练多个深度神经网络，以便在第二层学习系统状态与控制动作之间的函数关系。为了进一步提高深度神经网络的性能，设计并应用了一种简单而有效的数据聚合方法。然后，将这些经过训练的深度神经网络用作运动控制器，以实时生成反馈动作。通过数值结果来证明所提出的控制方案用于规划和引导AGV停车操纵的有效性和实时适用性。还提供了实验结果，以证明该算法在实际应用中的性能。
