https://repub.eur.nl/pub/134773/Repub_134773_O-A.pdf

Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda

Automated container terminal (ACT) is considered as port industry development direction, and accurate kinematic data (speed, volume, etc.) is essential for enhancing ACT operation efficiency and safety. Port surveillance videos provide much useful spatial-temporal information with advantages of easy obtainable, large spatial coverage, etc. In that way, it is of great importance to analyze automated guided vehicle (AGV) trajectory movement from port surveillance videos. Motivated by the newly emerging computer vision and artificial intelligence (AI) techniques, we propose an ensemble framework for extracting vehicle speeds from port-like surveillance videos for the purpose of analyzing AGV moving trajectory. Firstly, the framework exploits vehicle position in each image via a feature-enhanced scale-aware descriptor. Secondly, we match vehicle position and trajectory data from the previous step output via Kalman filter and Hungarian algorithm, and thus we obtain the vehicular imaging trajectory in a frame-by-frame manner. Thirdly, we estimate the vehicular moving speed in real-world via the help of perspective projection theory. The experimental results suggest that our proposed framework can obtain accurate vehicle kinematic data under typical port traffic scenarios considering that the average measurement error of root mean square deviation is 0.675 km/h, the mean absolute deviation is 0.542 km/h, and the Pearson correlation coefficient is 0.9349. The research findings suggest that cutting-edge AI and computer vision techniques can accurately extract on-site vehicular trajectory related data from port videos, and thus help port traffic participants make more reasonable management decisions. 

AI-Empowered Speed Extraction via Port-Like Videos for Vehicular Trajectory Analysis

Scheduling of container-handling equipment during the loading process at an automated container terminal

Integrated scheduling optimization of U-shaped automated container terminal under loading and unloading mode

For stochastic mixed-integer programs, we revisit the dual decomposition algorithm of Caroe and Schultz from a computational perspective with the aim of its parallelization. We address an important bottleneck of parallel execution by identifying a formulation that permits the parallel solution of the master program by using structure-exploiting interior-point solvers. Our results demonstrate the potential for parallel speedup and the importance of regularization (stabilization) in the dual optimization. Load imbalance is identified as a remaining barrier to parallel scalability.

On Parallelizing Dual Decomposition in Stochastic Integer Programming

The efficiency of automated container terminals primarily depends on the synchronization of automated-guided vehicles (AGVs) and automated cranes. Accordingly, we study the integrated rail-mounted yard crane and AGV scheduling problem as a multi-robot coordination and scheduling problem in this paper. Based on a discretized virtualized network, we propose a multi-commodity network flow model with two sets of flow balance constraints for cranes and AGVs. In addition, two side constraints are introduced to deal with inter-robot constraints to reflect the complex interactions among terminal agents accurately. The Alternating Direction Method of Multipliers (ADMM) method is adopted in this study as a market-driven approach to dualize the hard side constraints; therefore, the original problem is decomposed into a set of crane-specific and vehicle-specific subtasks. The cost-effective solutions can be obtained by iteratively adjusting both the primal and dual costs of each subtask. We also compare the computational performance of the proposed solution framework with that of the resource-constrained project scheduling problem (RCPSP) model using commercial solvers. Comparison results indicate that our proposed approach could efficiently find solutions within 2% optimality gaps. Illustrative and real-world instances show that the proposed approach effectively serves the accurate coordination of AGVs and cranes in automated terminals.

Yard crane and AGV scheduling in automated container terminal: A multi-robot task allocation framework

At present, the existing dynamic OD estimation methods in an urban rail transit network still need to be improved in the factors of the time-dependent characteristics of the system and the estimation accuracy of the results. This study focuses on predicting the dynamic OD demand for a time of period in the future for an urban rail transit system. We propose a nonlinear programming model to predict the dynamic OD matrix based on historic automatic fare collection (AFC) data. This model assigns the passenger flow to the hierarchical flow network, which can be calibrated by backpropagation of the first-order gradients and reassignment of the passenger flow with the updated weights between different layers. The proposed model can predict the time-varying OD matrix, the number of passengers departing at each time, and the travel time spent by passengers, of which the results are shown in the case study. Finally, the results indicate that the proposed model can effectively obtain a relatively accurate estimation result. The proposed model can integrate more traffic characteristics than traditional methods and provides an effective and hierarchical passenger flow estimation framework. This study can provide a rich set of passenger demand for advanced transit planning and management applications, for instance, passenger flow control, adaptive travel demand management, and real-time train scheduling.

/pdf/dynamic-origin-destination-matrix-estimation-based-on-urban-4culazdgmn.pdf

Dynamic Origin-Destination Matrix Estimation Based on Urban Rail Transit AFC Data: Deep Optimization Framework with Forward Passing and Backpropagation Techniques

In this study, we focused on the evolution of passenger flow in an urban rail transit network in both temporal and spatial dimensions under the event of station closure. We constructed an extended space-time-state hyper network so that we could utilize a better-defined three-dimensional solution space to describe passenger behaviors and pedestrian characters. The space-time-state network was extended by adding dummy arcs and setting the “block space-time domain” based on the original spatiotemporal network. Based on the extended space-time-state hyper network, a passenger flow evolution model that aimed to minimize the usage cost during station closure was established considering the strict capacity constraints (including corridor passing capacity, platform load capacity and train transport capacity). For a large-scale urban rail network, we developed a decomposition solution framework in which the Lagrangian relaxation and semi-assignment algorithms were adopted. A real-world instance was implemented based on the Beijing subway network with complete smart card data for each passenger for his/her origin and destination, while the specific space-time trajectories of all passengers and time-dependent passenger volumes in trains and transfer corridors were estimated. The results of a numerical experiment suggest that the proposed passenger flow evolution method can provide a rich set of passenger volume inferences for advanced transit planning and management applications, such as passenger flow control, passenger flow guidance, and real- time train scheduling.

Temporal and Spatial Evolution of Passenger Flow in an Urban Rail Transit Network During Station Closure

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Yard crane and AGV scheduling in automated container terminal: A multi-robot task allocation framework

Dynamic Origin-Destination Matrix Estimation Based on Urban Rail Transit AFC Data: Deep Optimization Framework with Forward Passing and Backpropagation Techniques

Temporal and Spatial Evolution of Passenger Flow in an Urban Rail Transit Network During Station Closure