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MTS/IEEE Oceans Seattle Conference (Oceans Seattle)

作者： Zang, Wenchuan Nie, Yunli Song, Dalei Guo, Tingting Li, Kunqian Ocean Univ China Coll Informat Sci & Engn Qingdao Peoples R China Ocean Univ China Coll Engn Qingdao Peoples R China

ISBN: (纸本)9780578576183

This paper presents a novel approach about constraining trajectories of underwater gliders based on Deep Q Network(dqn) and the dynamic model of gliders. In terms of dynamics modeling, we choose the angle of attack and roll angle as the input of the glider' movement simulation, the profile motions in the vertical plane is manipulated by the angle of attack, and the gyratory motion is accomplished by controlling the roll angle. Since different roll angles allow the glider to turn with different radius, it can be used to counteract the influence of current on the motion state of the glider. And the determination of the proper roll angle to make the glider's trajectory consistent with the preset route is a sequential decision problem. We use the glider's motion space discretization and apply the deep reinforcement learning theory to acquire the optimal roll angle, thus finally achieve the goal that improves the accuracy of the glider's *** simulation results show that the proposed method has better performance and advantage to deploy compared with the conventional PID method.

关键词： underwater gliders anti-currents trajectory constraining dqn algorithm

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A* guiding dqn algorithm for automated guided vehicle pathfinding problem of robotic mobile fulfillment systems

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期178卷

作者： Luo, Lei Zhao, Ning Zhu, Yi Sun, Yangjun Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China

This paper proposes an A* guiding deep Q-network (AG-dqn) algorithm for solving the pathfinding problem of an automated guided vehicle (AGV) in a robotic mobile fulfillment system (RMFS), that is, a parts-to-picker storage system with numerous AGVs replacing manual labor to improve the efficiency of picking work in warehouses. The pathfinding problem in an RMFS has characteristics such as changing scenes, narrow spaces, and significant decision-making time requirements. The A* algorithm and its variants have been widely used to address this problem. In this paper, we propose a reinforcement learning algorithm for a single AGV that uses the A* algorithm to guide the dqn algorithm. This makes the training process faster and requires less decision -making time than the A* algorithm. The trained neural network in the AG-dqn algorithm requires only the layout information of the current system to guide the AGV to complete a series of randomly assigned tasks. We used the AG-dqn algorithm to control the AGV pathfinding and complete tasks at different scales and layouts of the RMFS models, including traditional rectangular layouts and certain special layouts (e.g., fishbone layouts). The results show that the AG-dqn can train the AGV to find the correct shortest path to complete all tasks in less training time than the standard dqn algorithm. In addition, the decision-making time of the AG-dqn is less than that of the A* algorithm. The AG-dqn algorithm saved 49.92% and 71.51% of the decision-making time for the small-and large-scale RMFS models, respectively. Thus, the AG-dqn algorithm offers valuable insights into AGV control in an RMFS.

关键词： RMFS Pathfinding Reinforcement learning dqn algorithm A* algorithm

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Research on relieve of traffic congestion based on optimization of dqn algorithm in CamTra recognition system

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年

作者： Bai, Bing Li, Yangchen Ding, Shuya Qiao, Lei Wu, Yan Li, Xiaozheng Zhejiang Univ Sci & Technol Sch Mech & Energy Engn Hangzhou 310023 Peoples R China

This research presents a method of calculation for traffic signal plans based on the dqn (Deep Q-Leaning Network) algorithm to relieve traffic congestion in the city during rush hour. It is a very important part of our independently developed Camera Tracking System (CamTra), which has been applied in various fields such as traffic big data processing, drowning warning, factory intelligence optimization, and precise logistics simulation. With the increased number of cars, the occurrence of traffic congestion is rapidly increasing. The most important difficulty addressed in this paper is the analysis of the signal plan for tempo (speed and spacing) and green wave transmission. In addition to this, the research further optimized other accompanying factors such as action space and network structure, to achieve the best adaptability for signals. With the increase of adaptability for signal, the effectiveness of the epsilon-greedy strategy has also been further enhanced. The improved algorithm associates the epsilon-greedy strategy with the cumulative reward sequence, which realizes the adaptive control of the epsilon-greedy strategy and balances the relationship between exploration and performance. Finally, with the experiment of SUMO (Simulation of Urban Mobility), the performance of the enhanced dqn algorithm in single- and double-crossroads traffic signal control was verified. In comparison with the traditional static signal strategy, the enhanced dqn algorithm performed better in relieving traffic congestion.

关键词： Relieve traffic congestion green wave dqn algorithm epsilon-greedy strategy experiment of SUMO

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Automatic Driving Operation Strategy of Urban Rail Train Based on Improved dqn algorithm

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Journal on Artificial Intelligence 2023年第1期5卷 113-129页

作者： Tian Lu Bohong Liu School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou730070China

To realize a better automatic train driving operation control strategy for urban rail trains,an automatic train driving method with improved dqn algorithm(classical deep reinforcement learning algorithm)is proposed as a research ***,the train control model is established by considering the train operation ***,the dueling network and Ddqn ideas are introduced to prevent the value function overestimation ***,the priority experience playback and“restricted speed arrival time”are used to reduce the useless experience *** experiments are carried out to verify the train operation strategy method by simulating the actual line *** the experimental results,the train operation meets the ATO requirements,the energy consumption is 15.75%more energy-efficient than the actual operation,and the algorithm convergence speed is improved by about 37%.The improved dqn method not only enhances the efficiency of the algorithm but also forms a more effective operation strategy than the actual operation,thereby contributing meaningfully to the advancement of automatic train operation intelligence.

关键词： dqn algorithm automatic train operation(ATO) operation strategy urban rail train

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Research on Constraining Strategies of Underwater Glider's Trajectory Under the Influence of Ocean Currents Based on dqn algorithm

Research on Constraining Strategies of Underwater Glider's T...

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OCEANS

作者： Wenchuan Zang Yunli Nie Dalei Song Tingting Guo Kunqian Li College of Information Science and Engineering Ocean University of China Qingdao China College of Engineering Ocean University of China Qingdao China

ISBN: (纸本)9781728114521

This paper presents a novel approach about constraining trajectories of underwater gliders based on Deep Q Network(dqn) and the dynamic model of gliders. In terms of dynamics modeling, we choose the angle of attack and roll angle as the input of the glider' movement simulation, the profile motions in the vertical plane is manipulated by the angle of attack, and the gyratory motion is accomplished by controlling the roll angle. Since different roll angles allow the glider to turn with different radius, it can be used to counteract the influence of current on the motion state of the glider. And the determination of the proper roll angle to make the glider's trajectory consistent with the preset route is a sequential decision problem. We use the glider's motion space discretization and apply the deep reinforcement learning theory to acquire the optimal roll angle, thus finally achieve the goal that improves the accuracy of the glider's trajectory. The simulation results show that the proposed method has better performance and advantage to deploy compared with the conventional PID method.

关键词： Underwater gliders Anti-currents Trajectory constraining dqn algorithm Gliders roll angle ANGLE OF ATTACK trajectory ocean currents Decision problem vertical plane Underwater Simulation results

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Intelligent vibration control of tensile cable based on deep reinforcement learning

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ADVANCES IN STRUCTURAL ENGINEERING 2025年第4期28卷 736-752页

作者： Zhang, Enqi Xiang, Sheng Cheng, Bin Li, Derui Shanghai Jiao Tong Univ Dept Civil Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Digital Maintenance Bldg andInfra Shanghai Peoples R China

Vibration of tensile cables commonly occurs in the engineering structures such as cable-stayed bridges, which may have negative effect on the driving comfort and safety, and further lead to the fatigue problems of the structure. This paper proposes a semi-active control strategy based on deep reinforcement learning and magneto-rheological (MR) damper, for the vibration control of tensile cables, and the corresponding algorithm have been developed. Through the interaction with the cable-damper system, the intelligent agent can evaluate every possible control parameter and achieve an effective control policy. Then, according to the real-time vibration state, the agent would determine an action current for the MR damper and thus change the damping coefficient of the damper, further make influences on the vibrating cable. Basically, the proposed strategy realizes the model-free semi-active control of cable vibrations. To validate the effectiveness of the proposed semi-active control strategy, a scale model test has been conducted, where the test cases of passive and semi-active control strategies are carried out and compared. Results show that, the semi-active control shows a prior performance in vibration reduction compared to the passive control strategy, with regard to the vibration profile, the vibration energy, as well as the energy dissipation of MR damper.

关键词： cable vibration semi-active control deep reinforcement learning MR damper dqn algorithm

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Stable routing protocol for unmanned aerial vehicle ad-hoc networks based on dqn-OLSR

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IET COMMUNICATIONS 2023年第1期17卷 73-85页

作者： Zeng, Youjun Zhou, Jie Liu, Youjiang Cao, Tao Yang, Dalong Liu, Yu Shi, Xianhua China Acad Engn Phys Inst Elect Engn Mianyang 621900 Sichuan Peoples R China

In unmanned aerial vehicle ad-hoc network (UANET), the network topology changes with time due to the movement of the unmanned aerial vehicles (UAVs), which brings great challenges to the design of the routing protocol. In traditional routing protocols, when UANET topology changes, nodes cannot dynamically update neighbour nodes and topology information, and routing table calculation cannot accurately reflect the actual transmission path. As a result, network cannot meet the quality of service (QoS) requirements such as low end-to-end delay, high throughput and low packet loss rate. This paper proposes a dynamically optimized link state routing (OLSR) protocol based on Deep Q-Network algorithm (dqn-OLSR). In this protocol, each node first adjusts the sending interval of Hello messages adaptively in real time, according to the position and speed information of its neighbour nodes. Then the protocol uses the dqn algorithm to dynamically adjust the flooding interval of topology control (TC) messages to improve the routing update capability of nodes. The simulation verifies that the UANETs under this protocol have higher throughput and less packet loss rate than ad-hoc on-demand distance vector (AODV), grid routing protocol (GRP) and OLSR protocols, at different movement speeds in random waypoint (RWP) and random walk mobile models. Under nomadic as well as pursue mobile models, dqn-OLSR performs consistently with OLSR QoS performance, with the best performance among all four protocols. By further adding positioning errors to the nodes, it shows that the proposed protocol has good robustness, and the QoS performance degradation keeps within a low level.

关键词： UANET topology unmanned aerial vehicle ad-hoc network OLSR QoS performance mobile ad hoc networks random waypoint stable routing protocol dqn algorithm quality of service Mobile robots dynamically optimized link state routing protocol ad-hoc on-demand distance vector autonomous aerial vehicles RWP AODV Protocols Mobile radio systems deep Q-network algorithm low packet loss rate topology control messages routing update capability unmanned aerial vehicles network topology changes OLSR protocols low end-to-end delay topology information Communication network design, planning and routing routing protocols telecommunication network topology Computer communications GRP grid routing protocol neighbour nodes random walk mobile models

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A novel fault-tolerant scheduling approach for collaborative workflows in an edge-IoT environment

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Digital Communications and Networks 2022年第6期8卷 911-922页

作者： Tingyan Long Yong Ma Lei Wu Yunni Xia Ning Jiang Jianqi Li Xiaodong Fu Xiangmi You Bo Zhang College of Computer Science Chongqing UniversityChongqing400030China School of Computer and Information Engineering Jiangxi Normal UniversityJiangxi330022China School of Mathematical Sciences University of Electronic Science and Technology of ChinaChengdu611731China Mashang Consumer Finance Co. Ltd.(MSCF)Chongqing401129China Global Energy Interconnection Research Institute Co.Ltd. Beijing102209China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyYunnan650500China CISDI Research&Development CO. LTDChongqing401122China

As a newly emerging computing paradigm, edge computing shows great capability in supporting and boosting 5G and Internet-of-Things (IoT) oriented applications, e.g., scientific workflows with low-latency, elastic, and on-demand provisioning of computational resources. However, the geographically distributed IoT resources are usually interconnected with each other through unreliable communications and ever-changing contexts, which brings in strong heterogeneity, potential vulnerability, and instability of computing infrastructures at different levels. It thus remains a challenge to enforce high fault-tolerance of edge-IoT scientific computing task flows, especially when the supporting computing infrastructures are deployed in a collaborative, distributed, and dynamic environment that is prone to faults and failures. This work proposes a novel fault-tolerant scheduling approach for edge-IoT collaborative workflows. The proposed approach first conducts a dependency-based task allocation analysis, then leverages a Primary-Backup (PB) strategy for tolerating task failures that occur at edge nodes, and finally designs a deep Q-learning algorithm for identifying the near-optimal workflow task scheduling scheme. We conduct extensive simulative case studies on multiple randomly-generated workflow and real-world edge-IoT server position datasets. Results clearly suggest that our proposed method outperforms the state-of-the-art competitors in terms of task completion ratio, server active time, and resource utilization.

关键词： Edge computing Fault tolerance dqn algorithm Primary-backup model

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Research on ATO Control Method for Urban Rail Based on Deep Reinforcement Learning

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IEEE ACCESS 2023年 11卷 5919-5928页

作者： Chen, Xiaoqiang Guo, Xiao Meng, Jianjun Xu, Ruxun Li, Shanshan Li, Decang Lanzhou Jiaotong Univ Mechatron T&R Inst Lanzhou 730070 Peoples R China Gansu Logist & Transportat Equipment Informat Tech Lanzhou 730070 Peoples R China Gansu Logist & Transportat Equipment Ind Tech Ctr Lanzhou 730070 Peoples R China Lanzhou Jiaotong Univ Sch Mech Engn Lanzhou 730070 Peoples R China

Aiming at the problems of punctuality, parking accuracy and energy saving of urban rail train operation, an intelligent control method for automatic train operation (ATO) based on deep Q network (dqn) is proposed. The train dynamics model is established under the condition of satisfying the safety principle and various constraints of automatic driving of urban rail train. Considering the transformation rules and sequences of working conditions between train stations, the agent in the dqn algorithm is used as the train controller to adjust the train automatic driving strategy in real time according to the train operating state and operating environment, and optimizes the generation of the train automatic driving curve. Taking the Beijing Yizhuang Subway line as an example, the simulation test results show that the dqn urban rail train control method reduces energy consumption by 12.32% compared with the traditional train PID control method, and improves the running punctuality and parking accuracy;at the same time, the dqn train automatically driving control method can adjust the train running state in real time and dynamically, and has good adaptability and robustness to the change of train running environment parameters.

关键词： Energy consumption Railway transportation Reinforcement learning Heuristic algorithms Target tracking Real-time systems Resource management Urban areas Public transportation Urban rail train dqn algorithm multi-objective optimization automatic driving

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A Deep Q-Learning Network for Dynamic Constraint-Satisfied Service Composition

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INTERNATIONAL JOURNAL OF WEB SERVICES RESEARCH 2020年第4期17卷 55-75页

作者： Yu, Xuezhi Ye, Chunyang Li, Bingzhuo Zhou, Hui Huang, Mengxing Hainan Univ Sch Comp Sci & CyberSpace Secur Haikou Hainan Peoples R China Hainan Univ Haikou Hainan Peoples R China

Traditional service composition methods usually address the constraint-satisfied service composition (CSSC) problem with static web services. Such solutions however are inapplicable to the dynamic scenarios where the services or their QoS values may change over time. Some recent studies are proposed to use reinforcement learning, especially, integrate the idea of Q-learning, to solve the dynamic CS SC problem. However, such Q-learning algorithm relies on Q-table to search for optimal candidate services. When the problem of CSSC becomes complex, the number of states in Q-table is very large and the cost of the Q-learning model will become extremely high. In this paper, the authors propose a novel solution to address this issue. By training a dqn network to replace the Q-table, this solution can effectively model the uncertainty of services with fine-grained QoS attributes and choose suitable candidate services to compose on the fly in the dynamic scenarios. Experimental results on both artificial and real datasets demonstrate the effectiveness of the method.

关键词： dqn algorithm Service Quality Web Service Composition

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