Xiaohua Wei

Transportation panoptic perception (TPP) is a fundamental capability for both on-board and roadside monitoring systems. In this paper, we propose an end-to-end lightweight multitask model, MT-TPPNet, which jointly performs three tasks: object detection, drivable area segmentation, and lane line segmentation. To accommodate task differences while sharing a common backbone, we introduce the Asymmetric Projection with Expanded-value (APEX) mechanism, which integrates attention mechanisms with different biases to enhance performance across various tasks. We further propose the Selective Channel–Spatial Coupling (${\mathrm{SC}}^2$) mechanism, which injects complementary frequency-band information into the channel-spatial coupled features. Additionally, by using a unified loss function to simultaneously handle detection and segmentation tasks, we eliminate the need for task-specific customizations, improving both training stability and deployment flexibility. Extensive experiments on self-collected field data and public benchmarks from roadway and railway scenarios demonstrate that MT-TPPNet consistently outperforms strong baselines in terms of mAP, mIoU, and FPS. In particular, MT-TPPNet achieves a mAP50 of 83.2% for traffic object detection, a mIoU of 91.6% for drivable-area segmentation, and an IoU of 28.9% for lane-line segmentation, demonstrating the effectiveness of the proposed approach.

Hydraulically driven flexible robotic arms (HDFRAs) play an indispensable role in industrial precision operations such as aerospace assembly and nuclear waste handling, owing to their high power density and adaptability to complex environments. However, inherent mechanical flexibility-induced vibrations, hydraulic nonlinear dynamics, and electromechanical coupling effects lead to multi-timescale control challenges, severely limiting high-precision trajectory tracking performance. The present study introduces a novel hierarchical control framework employing dual-timescale perturbation analysis, which effectively addresses the constraints inherent in conventional single-timescale control approaches. First, the system is decoupled into three subsystems via dual perturbation parameters: a second-order rigid-body motion subsystem (SRS), a second-order flexible vibration subsystem (SFS), and a first-order hydraulic dynamic subsystem (FHS). For SRS/SFS, an adaptive fast terminal sliding mode active disturbance rejection controller (AFTSM-ADRC) is designed, featuring a dual-bandwidth extended state observer (BESO) to estimate parameter perturbations and unmodeled dynamics in real time. A novel reaching law with power-rate hybrid characteristics is developed to suppress sliding mode chattering while ensuring rapid convergence. For FHS, a sliding mode observer-integrated sliding mode coordinated controller (SMO-ISMCC) is proposed, achieving high-precision suppression of hydraulic pressure fluctuations through feedforward compensation of disturbance estimation and feedback integration of tracking errors. The globally asymptotically stable property of the composite system has been formally verified through systematic Lyapunov-based analysis. Through comprehensive simulations, the developed methodology demonstrates significant improvements over conventional ADRC and PID controllers, including (1) joint tracking precision reaching ${10}^{-4}$ rad level under nominal conditions and (2) over 40% attenuation of current oscillations when subjected to stochastic disturbances. These results validate its superiority in dynamic decoupling and strong disturbance rejection.