Research and Application on Design of Underground Container Logistics System Based on Autonomous Container Truck

You, Kesi; Yu, Mingjian; Yu, Qifeng

doi:10.1088/1755-1315/330/2/022054

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…It also introduced the RORO definition, subsystems, as well as the associated logistics processes. To take advantage of the autonomy capabilities, K. You et al [19] proposed an automated freight transport system based on the autonomous container truck. The designed system operates in a dual-mode, mixing the autonomous driving, which is used in the terminal and connecting tunnels, with the manned driving mode, which is adopted within the operation area.…”

Section: Background 21 Related Workmentioning

confidence: 99%

Rolling Cargo Management Using a Deep Reinforcement Learning Approach

et al. 2021

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Loading and unloading rolling cargo in roll-on/roll-off are important and very recurrent operations in maritime logistics. In this paper, we apply state-of-the-art deep reinforcement learning algorithms to automate these operations in a complex and real environment. The objective is to teach an autonomous tug master to manage rolling cargo and perform loading and unloading operations while avoiding collisions with static and dynamic obstacles along the way. The artificial intelligence agent, representing the tug master, is trained and evaluated in a challenging environment based on the Unity3D learning framework, called the ML-Agents, and using proximal policy optimization. The agent is equipped with sensors for obstacle detection and is provided with real-time feedback from the environment thanks to its own reward function, allowing it to dynamically adapt its policies and navigation strategy. The performance evaluation shows that by choosing appropriate hyperparameters, the agents can successfully learn all required operations including lane-following, obstacle avoidance, and rolling cargo placement. This study also demonstrates the potential of intelligent autonomous systems to improve the performance and service quality of maritime transport.

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Section: Background 21 Related Workmentioning

confidence: 99%

Rolling Cargo Management Using a Deep Reinforcement Learning Approach

et al. 2021

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“…4 is a multilayer, which comprises various elements of the control system overlaid in "layers" into a single automated transport control system. The multi-layer model of automated control involves solving a wide range of tasks: start, stop, acceleration, information analysis, signal processing, observing timing and distance limits, control, prevention and elimination of emergency situations, forecasting, and the like (You et al, 2019;Africa et al, 2017;Naghiyev et al, 2017;Gaspari et al, 2019).…”

Section: Analysis Of the Application Of Automated Control Systems In Order To Increase The Economic Effect Of Underground Operationmentioning

confidence: 99%

Organizational and economic mechanism of the automatic underground train operation system

al.¹

2021

Int. j. adv. appl. sci.

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This work deals with the provision of underground, as one of the main modes of public transport, with automated control systems, in particular the provision of automated train management. It defines that the underground is one of the most progressive modes of transport, which is related to its environmental safety, comfort, and speed of movement, as this mode of transport does not intersect with other transport and pedestrian routes, which excludes traffic blocks. Underground is an important element of urbanized spaces, for it serves large passenger traffic. The article presents an algorithm for the creation of an automated control system for the underground train system. Based on this algorithm, a model of building an organizational and economic mechanism for automation of control systems has been developed. The diagram of organizational and technical implementation of the automated system of underground train management is presented. An economic analysis of the effectiveness of the application of the automated control system of underground trains was carried out. The recoupment on the acquisition and installation of automation systems is very high. The application of the automated system will increase the capacity of the underground by optimizing the traffic schedules, which will contribute to increasing its profitability. It is also useful to determine the reduction of electricity consumption due to the change in the dynamics of acceleration and braking systems, which is important, as the underground is a powerful enterprise. In the future, a promising direction is to streamline the coordination of train schedules and other modes of land public transport.

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Research and Application on Design of Underground Container Logistics System Based on Autonomous Container Truck

Cited by 2 publications

References 2 publications

Rolling Cargo Management Using a Deep Reinforcement Learning Approach

Rolling Cargo Management Using a Deep Reinforcement Learning Approach

Organizational and economic mechanism of the automatic underground train operation system

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