The application of Cyber-Physical System (CPS) and edge computing in smart logistics systems has greatly improved the work efficiency. This paper mainly focuses on the planning and scheduling problems in the Cyber-Physical System based multiple Automatic Guided Vehicles (multi-AGVs) logistics system. A virtual network map with uneven time constraints is firstly proposed, which realizes the control and interaction of actual work in the control layer. Then, an improved A* path planning algorithm based on the characteristics of the path network model is implemented. A novel traffic control method is added to the planning process to achieve time-sensitive and proactive scheduling and collision avoidance. Results of the numerical experiments reveal that the proposed Cyber-Physical System based heuristic method could be effectively applied to the multiple automatic guided vehicles logistics system to further improve system security as well as work efficiency.

Dear Editor, This letter deals with the tracking problem of quadrotors subject to external disturbances and visibility constraints by designing a robust model predictive control (RMPC) scheme. According to the image-based visual servoing (IBVS) method, a virtual camera is constructed to express image moments of the tracking target. Furthermore, we design time-varying visibility constraints to cater to UAV attitudes in the RMPC optimization problem so that the tracking target always stays within the field of view (FoV) of the aerial camera. Moreover, the RMPC algorithm is proved to be recursively feasible, and the closed-loop system is stable. Finally, numerical experiments verify the efficacy of the proposed method.

The application of vision-guided multiwarehouse automated guided vehicles (AGVs) is becoming more extensive. This article mainly studies the position measurement and corresponding path planning of AGV guided by visual sensors. An improved vehicle-mounted visual sensor model is proposed, which effectively reduces the cost of the sensor and achieves position prediction. The proposed manual coding landmark significantly improves the decoding speed and accuracy while ensuring the information coding capacity. An improved heuristic path planning algorithm combined with the visual sensor system is implemented, considering the planning effectiveness and directionality. Finally, simulations and experiments verify the reliability of the visual guidance method in position measurement and the advancement of the planning strategy. Compared with the traditional 2-D code and bottom visual model, the improved visual sensor position measurement effectively reduces motion blur and corresponding pose deviation. The decoding speed and accuracy are improved by more than 41.21% and 53.85%, respectively. Planning performance comparison is achieved between the same type of heuristic algorithms (A*, D*, and algorithm deployment). The significant improvement in core system indicators includes task efficiency (up to 70.02% improvement), parking time (up to 72% reduction), deadlock (up to 85.05% reduction), and planning time (up to 89.39% reduction).

For inertial platforms with unknown model parameters and internal information, traditional model-free controllers fail to resist external vibrations solely based on the platform gyroscope, deteriorating the performance of inertial platforms. Therefore, we apply the light gradient boosting machine (LightGBM) to identify an end-to-end platform model, followed by proposing a data-driven MPC scheme to improve the control performance. Furthermore, an expectation maximization (EM) method is designed to solve the optimization problems with non-differentiable identification models, which are challenges for the traditional gradient descent-based optimizer. In addition, an adaptive compensation strategy is designed for generalizing the data-driven control scheme to different external vibrations. Finally, experimental results demonstrate the feasibility, efficacy, and generalization ability of the proposed method.