Article
Computer Science, Information Systems
Lijun He, Ben Liang, Jiandong Li, Min Sheng
Summary: This paper investigates joint observation and transmission scheduling in agile satellite networks (ASNs) to accommodate more imaging data. The problem is formulated as an integer linear programming (ILP) and solved efficiently using semidefinite relaxation (SDR).
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Automation & Control Systems
Guansheng Peng, Guopeng Song, Yongming He, Jing Yu, Shang Xiang, Lining Xing, Pieter Vansteenwegen
Summary: This study focuses on the scheduling problem of agile Earth observation satellites. A time-dependent transition time model is proposed and proven to satisfy certain rules. Based on this model, a novel heuristic algorithm is developed, which outperforms existing algorithms in terms of solution quality and computation time according to extensive experiments.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Engineering, Aerospace
Tobias Stollenwerk, Vincent Michaud, Elisabeth Lobe, Mathieu Picard, Achim Basermann, Thierry Botter
Summary: A comparison study was conducted between classical optimization methods and a D-Wave 2000Q quantum annealer for scheduling Earth observation satellites, revealing that the quantum annealer can find optimal solutions faster but with rapidly degrading solution quality due to limited precision.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Peng Wang, Hongyan Li, Binbin Chen, Shun Zhang
Summary: Earth observation systems are crucial in various critical applications, but the challenge lies in transmitting the massive amount of data back to Earth. Inter-satellite communication offers a promising solution to enhance the data throughput. Our work focuses on two key design factors: supporting on-demand scheduling of inter-satellite communication and optimizing the scheduling of observation and transmission missions. Through rigorous study and modeling, we find that using inter-satellite communication can significantly increase the data throughput.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2022)
Article
Environmental Sciences
Yanxiang Feng, Ruipeng Zhang, Sida Ren, Shuailin Zhu, Yikang Yang
Summary: This study focuses on the problem of distributed observation scheduling in the AEOS constellation and proposes a PIDSM method based on a performance impact algorithm. A new fitness function is defined to maximize the profit sum and consider system load balancing. Experimental results show that the PIDSM can schedule more targets, reduce communication overhead, and achieve higher fitness values than existing algorithms.
Article
Environmental Sciences
Changyuan He, Yunfeng Dong, Hongjue Li, Yingjia Liew
Summary: With the rapid development of agile Earth observation satellites (AEOSs), these satellites are able to conduct more high-quality observation missions. Nevertheless, while completing these missions takes up more data transmission and electrical energy resources, it also increases the coupling within each satellite subsystem. To address this problem, we propose a reasoning-based scheduling method for an AEOS under multiple subsystem constraints.
Article
Engineering, Aerospace
Xin Wang, Jian Wu, Zhong Shi, Fanyu Zhao, Zhonghe Jin
Summary: This paper proposes a deep reinforcement learning-based algorithm for autonomous mission planning of high and low orbiting agile Earth observation satellites. The algorithm achieves simultaneous scheduling of multiple satellites and reduces reasoning time significantly compared to other algorithms while maintaining a small difference in revenue rate.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Israel Leyva-Mayorga, Beatriz Soret, Petar Popovski
Summary: The future of wireless communications will heavily rely on dense satellite constellations deployed in low Earth orbit (LEO). While efforts to integrate satellites into 5G networks are promising, challenges such as dynamic inter-plane inter-satellite links need to be addressed. The proposed algorithms in this paper show significant improvements in both interference-free and worst-case scenarios for interference, increasing the sum of rates in the constellation by up to 115% and 71% respectively.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2021)
Article
Geochemistry & Geophysics
Li Dalin, Wang Haijiao, Yang Zhen, Gu Yanfeng, Shen Shi
Summary: The letter proposes a multiagent deep reinforcement learning (MADRL)-based method for scheduling real-time multisatellite cooperative observation. This method reduces communication overhead, improves response time, and achieves better use of satellite resources compared to existing algorithms like the Contract Net Protocol.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2021)
Article
Engineering, Aerospace
Qingyu Qu, Kexin Liu, Xijun Li, Yunfan Zhou, Jinhu Lu
Summary: This article develops an intelligent Earth observation satellites (EOSs) scheduling framework using imitation learning based on mixed integer linear programming (MILP), which includes preprocessing, modeling, and solving processes. The framework effectively solves the complex combinatorial optimization problem of EOSs scheduling and improves reliability and efficiency.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Pietro Cassara, Alberto Gotta, Mario Marchese, Fabio Patrone
Summary: Mega-LEO satellite constellations are proving their feasibility and potential applications by providing worldwide internet access equality and offering the possibility of offloading computing tasks to an orbital edge platform, leading to improved performance.
IEEE COMMUNICATIONS MAGAZINE
(2022)
Article
Engineering, Aerospace
Mayer Humi, Thomas Carter
Summary: This paper examines the Keplerian orbits of satellites in the Earth-Moon system, which provide approximate solutions to the fundamental equations of motion. Although satellites in these orbits require thrust to stay in orbit, they may be of interest when practical constraints need to be met.
Article
Computer Science, Information Systems
Jian Wu, Feng Yao, Yanjie Song, Lei He, Fang Lu, Yonghao Du, Jungang Yan, Yuning Chen, Lining Xing, Junwei Ou
Summary: To address the time-dependent agile earth observation satellite (AEOS) scheduling problem more effectively, a frequent pattern-based parallel search (FPBPS) algorithm is proposed. The algorithm consists of a parallel local search procedure, a competition-based algorithm, an operator adaptive selection procedure, and a new solutions construction method based on frequent pattern mining. Extensive experiments have shown that the FPBPS algorithm outperforms other comparison algorithms in terms of solution quality, computation time, and robustness.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Hardware & Architecture
Xinru Mi, Chungang Yang, Yanbo Song, Zhu Han, Mohsen Guizani
Summary: Integrated satellite-terrestrial networks (ISTNs) offer a new networking paradigm for achieving continuous, seamless, and global coverage, receiving significant research attention. The intelligent and efficient management of multidimensional resources in the ISTN environment is crucial, and the matching game provides a potential solution. This article discusses the requirements, challenges, and proposes a matching game-based framework for resource management in ISTNs.
IEEE WIRELESS COMMUNICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Guanming Zeng, Yafeng Zhan, Haoran Xie, Chunxiao Jiang
Summary: In this paper, a networked telemetry system is designed to meet the monitoring requirements of mega constellations in the upcoming 6G communication era. Data is transmitted through the inter-satellite-links of LEO and MEO satellites. The resource allocation problem is decomposed using the block coordinate descent method and effectively increases the transmitted data amount of the system.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2022)
Article
Management
Bin Ji, Zheng Zhang, Samson S. Yu, Saiqi Zhou, Guohua Wu
Summary: In this paper, a many-to-many heterogeneous vehicle routing problem with cross-docking and two-dimensional loading constraints (2L-MVRPCD) is addressed, considering the practical applications of two-dimensional loading on vehicle scheduling and many-to-many supply-demand relationships. A mixed integer linear programming (MILP) model is developed to solve small-scale instances, while two hybrid optimization heuristic algorithms are proposed for large-scale instances. The numerical results show that both the MILP model and the heuristics achieve good performance for different scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Shuai Deng, Duohong Zhou, Guohua Wu, Ling Wang, Ge You
Summary: The article introduces an effective way to solve vehicle capacity utilization in China using logistics platforms and information sharing, and discusses the conflicts of interest among freight transportation participants. A game model is developed to analyze their interactions and strategies, and suggestions for achieving the sustainability of freight transportation are provided.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xinwei Wang, Alexander Edward Ian Brownlee, Michal Weiszer, John R. Woodward, Mahdi Mahfouf, Jun Chen
Summary: Airports and their related operations are causing major concerns in air traffic management system due to predictability, safety, and environmental issues. This article proposes a new interval type-2 fuzzy logic-based map matching algorithm to optimize airport ground movement. Experimental results show that the designed fuzzy rules have the potential to handle map matching uncertainties, and the extra checking step can effectively improve map matching accuracy. The proposed algorithm is demonstrated to be robust with a map matching accuracy of over 96% without compromising the run time.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Jiaxing Li, Guohua Wu, Tianjun Liao, Mingfeng Fan, Xiao Mao, Witold Pedrycz
Summary: A novel task scheduling framework is proposed to optimize the task scheduling problem in data relay satellite networks using deep reinforcement learning. Mathematical and decision models are constructed and a policy network with encoder and decoders is designed to solve the problem. Extensive experiments demonstrate the effectiveness and generalization of the framework.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Automation & Control Systems
Zhongqiang Ma, Guohua Wu, Bin Ji, Ling Wang, Qizhang Luo, Xinjiang Chen
Summary: This study addresses the commodity storage assignment problem and proposes a scattered storage policy based on commodity classification to improve order picking efficiency. By utilizing a mixed-integer programming model and a novel algorithm, the proposed approach performs well in solving CSAP-SCSPCC and outperforms existing methods.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Mingfeng Fan, Yaoxin Wu, Tianjun Liao, Zhiguang Cao, Hongliang Guo, Guillaume Sartoretti, Guohua Wu
Summary: In this paper, a UAV routing problem in the presence of multiple charging stations is studied, aiming to minimize the total distance traveled by the UAV during traffic monitoring. A deep reinforcement learning based method is presented, which incorporates a multi-head heterogeneous attention mechanism for automatically constructing the route and considering energy consumption. Results show that the method outperforms conventional algorithms in terms of solution quality and runtime, and exhibits strong generalized performance on different problem instances.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Binghong Liu, Chenxi Liu, Mugen Peng
Summary: This paper proposes a novel cloud-edge framework to facilitate mobile edge computing (MEC) in UAV networks. The edge UAVs, together with the cloud, provide caching and computing services for terrestrial users. The proposed algorithm of sequential convex programming (SCP) and sequential quadratic programming (SQP) based deep Q-learning (SS-DQN) improves system performance significantly compared to two benchmark schemes.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Automation & Control Systems
Chao Han, Yi Gu, Guohua Wu, Xinwei Wang
Summary: Agile satellites, with stronger attitude maneuvering capability, are the new generation of Earth observation satellites. Cloud coverage has a significant impact on satellite observation missions, making scheduling of multiple agile satellites complicated. Introducing cloud coverage uncertainty further increases the scheduling complexity, motivating the development of an improved heuristic algorithm for the problem.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Aerospace
Guohua Wu, Xiao Mao, Yingguo Chen, Xinwei Wang, Wenkun Liao, Witold Pedrycz
Summary: To better utilize Earth-observation resources (EORs), researchers have developed a divide-and-conquer framework (DCF) for coordinated scheduling of both air and space observation resources, such as satellites and unmanned aerial vehicles. The DCF decomposes the scheduling problem into task allocation and task scheduling subproblems, which are solved using a coordination planner and subplanners respectively. Experimental results show that the proposed algorithm, SA-VNA, outperforms peer algorithms, indicating that DCF with SA-VNA can significantly improve the efficiency of space-air resource networks.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Aijuan Song, Guohua Wu, Ponnuthurai Nagaratnam Suganthan, Witold Pedrycz
Summary: A variable reduction strategy is an effective method to speed up the optimization process of evolutionary algorithms by simplifying the optimization problems. However, the current manual implementation of the strategy is trial-and-error-based. To improve its efficiency and applicability, we propose a variable reduction optimization problem to represent decision spaces with the smallest sets of variables. Then, we design an automatic variable reduction algorithm based on heuristic rules to address this problem.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Civil
Fangyu Hong, Guohua Wu, Qizhang Luo, Huan Liu, Xiaoping Fang, Witold Pedrycz
Summary: This paper proposes a novel package pickup and delivery mode using drones and automatic devices. The mode utilizes free areas on top of residential buildings for package delivery and pickup and employs drones for transportation. The integrated scheduling problem is solved using a simulated-annealing-based two-phase optimization approach. The effectiveness of the proposed approach is verified through extensive experiments and comparative studies.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Huan Liu, Guohua Wu, Ling Zhou, Witold Pedrycz, Ponnuthurai Nagaratnam Suganthan
Summary: Unmanned aerial vehicles (UAVs) face challenges in path planning in 3-D urban environments. This paper introduces a tangent-based (3D-TG) method that constructs a tangent graph to generate sub-paths for UAVs to bypass obstacles efficiently. The experimental results demonstrate the effectiveness of 3D-TG in both static and dynamic environments, as well as its ability to generate collision-free paths through simple mazes.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Qizhang Luo, Guohua Wu, Anupam Trivedi, Fangyu Hong, Ling Wang, Dipti Srinivasan
Summary: To efficiently implement the truck-drone collaborative logistics system, a multi-objective truck-drone collaborative routing problem with delivery and pick-up services (MCRP-DP) is introduced. An objective space decomposition-based multi-objective evolutionary algorithm with adaptive resource allocation (ODEA-ARA) is proposed to solve MCRP-DP. ODEA-ARA outperforms its competitors in terms of performance, and several useful managerial insights are presented.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Aerospace
Yi Gu, Chao Han, Yuhan Chen, Shenggang Liu, Xinwei Wang
Summary: This article focuses on the observation scheduling problem for large region targets of Earth observation satellites (EOSs). A rapid coverage calculation method and a nonlinear integer programming model are developed, and a proposed greedy initialization-based resampling particle swarm optimization (GI-RPSO) algorithm is used to solve the model. Extensive experiments show that the proposed method can improve the scheduling result significantly compared to traditional methods.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
