Xueying Yang
ABSTRACT
Complex heterogeneous scenarios with multiple mission requirement relationships, poor model scalability, resource conflicts during mission planning are the serious challenges currently facing the field of multi-satellite imaging mission planning (MSIMP). To solve this difficult problem, this paper proposes an Objective task-matching strategy and Improved adaptive differential evolution algorithm (OTMS-IADE). Firstly, the target task matching strategy for MSIMP in complex heterogeneous scenarios is constructed for multi-user, multi-satellite and multi-task situations, which overcomes the problem of poor scalability of the planning model in complex heterogeneous scenarios, and reduces the loss of resources caused by inappropriate task allocation; Secondly, to address the problem of low execution efficiency and long planning time due to large MSIMP solution space and complex constraints in complex heterogeneous scenarios, an improved adaptive differential evolution algorithm is proposed to reasonably trade-off the spatial search performance and the spatial exploitation performance to enhance the algorithm solution efficiency. Simulation experiments show that the OTMS-IADE algorithm for processing complex heterogeneous scenarios MSIMP has obvious advantages regarding task importance optimization and timeliness.
- Atul Adya, Paramvir Bahl, Jitendra Padhye, Alec Wolman, and Lidong Zhou. 2004. A multi-radio unification protocol for IEEE 802.11 wireless networks. In Proceedings of the IEEE 1st International Conference on Broadnets Networks (BroadNets’04) . IEEE, Los Alamitos, CA, 210–217. https://doi.org/10.1109/BROADNETS.2004.8Google Scholar
Digital Library
- Qiu, W.; Xu, C.; Ren, Z.; Teo, K.L. Scheduling and Planning Framework for Time Delay Integration Imaging by Agile Satellite. IEEE Trans. Aerosp. Electron. Syst. 2022, 58, 189–205, doi:10.1109/TAES.2021.3098101.Google ScholarCross Ref
- G. Zhang, X. Li, G. Hu, Z. Zhang, J. An, W. Man, Mission Planning Issues of Imaging Satellites: Summary, Discussion, and Prospects, Int. J. Aerosp. Eng. 2021 (2021) 1–20. https://doi.org/10.1155/2021/7819105Google ScholarCross Ref
- Y. Song, J. Ou, J. Wu, Y. Wu, L. Xing, and Y. Chen, “A cluster-based genetic optimization method for satellite range scheduling system,” Swarm Evol. Comput., vol. 79, no. 4, p. 101316, Jun. 2023, doi: 10.1016/j.swevo.2023.101316.Google ScholarCross Ref
- X. Han, M. Yang, S. Wang, and T. Chao, “Continuous monitoring scheduling for moving targets by Earth observation satellites,” Aerosp. Sci. Technol., vol. 140, no. 4, p. 108422, Sep. 2023, doi: 10.1016/j.ast.2023.108422.Google ScholarCross Ref
- Q. Qu, K. Liu, X. Li, Y. Zhou, and J. Lu, “Satellite Observation and Data-Transmission Scheduling using Imitation Learning based on Mixed Integer Linear Programming,” IEEE Trans. Aerosp. Electron. Syst., vol. 140, no. 4, pp. 1–25, Sep. 2022, doi: 10.1109/TAES.2022.3210073.Google ScholarCross Ref
- Y. Yu, Q. Hou, J. Zhang, and W. Zhang, “Mission scheduling optimization of multi-optical satellites for multi-aerial targets staring surveillance,” J. Franklin Inst., vol. 357, no. 13, pp. 8657–8677, Sep. 2020, doi: 10.1016/j.jfranklin.2020.06.023.Google ScholarCross Ref
- C. Li, W. Xu, L. Xu, and Y. Wang, “An approach to multi-satellite TT&C resource scheduling based on multi-agent technology and comprehensive weighted priority determination method,” J. Phys. Conf. Ser., vol. 1812, no. 1, p. 012001, Feb. 2021, doi: 10.1088/1742-6596/1812/1/012001.Google ScholarCross Ref
- J. Liang, Y. Zhu, Y. Luo, J. Zhang, H. Zhu, A precedence-rule-based heuristic for satellite onboard activity planning, Acta Astronaut. 178 (2021) 757–772. https://doi.org/10.1016/j.actaastro.2020.10.020Google ScholarCross Ref
- W. Zhu, X. Hu, W. Xia, and H. Sun, “A three-phase solution method for the scheduling problem of using earth observation satellites to observe polygon requests,” Comput. Ind. Eng., vol. 130, no. 1, pp. 97–107, Apr. 2019, doi: 10.1016/j.cie.2019.02.014.Google ScholarDigital Library
- C. Han, Y. Gu, G. Wu, and X. Wang, “Simulated Annealing-Based Heuristic for Multiple Agile Satellites Scheduling Under Cloud Coverage Uncertainty,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 53, no. 5, pp. 2863–2874, May 2023, doi: 10.1109/TSMC.2022.3220534.Google ScholarCross Ref
- T. Wang, Q. Luo, L. Zhou, and G. Wu, “Space division and adaptive selection strategy based differential evolution algorithm for multi-objective satellite range scheduling problem,” Swarm Evol. Comput., vol. 83, no. 5, p. 101396, Dec. 2023, doi: 10.1016/j.swevo.2023.101396.Google ScholarCross Ref
- Z. E., R. Shi, L. Gan, H. Baoyin, and J. Li, “Multi-satellites imaging scheduling using individual reconfiguration based integer coding genetic algorithm,” Acta Astronaut., vol. 178, no. 5, pp. 645–657, Jan. 2021, doi: 10.1016/j.actaastro.2020.08.041.Google ScholarCross Ref
- J. Liang, Y. Zhu, Y. Luo, J. Zhang, and H. Zhu, “A precedence-rule-based heuristic for satellite onboard activity planning,” Acta Astronaut., vol. 178, no. 5, pp. 757–772, Jan. 2021, doi: 10.1016/j.actaastro.2020.10.020.Google ScholarCross Ref
- M. Ghasemi, M. Zare, P. Trojovský, A. Zahedibialvaei, and E. Trojovská, “A hybridizing-enhanced differential evolution for optimization,” PeerJ Comput. Sci., vol. 9, no. 5, p. e1420, Jun. 2023, doi: 10.7717/peerj-cs.1420.Google ScholarCross Ref
- H. Zhang, J. Sun, K. C. Tan, and Z. Xu, “Learning Adaptive Differential Evolution by Natural Evolution Strategies,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 7, no. 3, pp. 872–886, Jun. 2023, doi: 10.1109/TETCI.2022.3210927.Google ScholarCross Ref
- J.-Y. Li, K.-J. Du, Z.-H. Zhan, H. Wang, and J. Zhang, “Distributed Differential Evolution With Adaptive Resource Allocation,” IEEE Trans. Cybern., vol. 53, no. 5, pp. 2791–2804, May 2023, doi: 10.1109/TCYB.2022.3153964.Google ScholarCross Ref
Index Terms
- Objective task matching strategy for Multi-Satellite Imaging Mission Planning in complex heterogeneous scenarios
Recommendations
Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms
Due to recent booming of unmanned air vehicles (UAVs) technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent ...
Multi-objective TT&C Mission Planning Technique for On-Orbit Service
ISCC-C '13: Proceedings of the 2013 International Conference on Information Science and Cloud Computing CompanionThe multi-objective TT&C (Tracking, Telemetry and Command) mission planning technique is studied based on the background of both ground-based and space-based TT&C mission to the OOS (On-Orbit Service) type of "one-station to multi-objective". Firstly, ...
Applications of multi-objective evolutionary algorithms to air operations mission planning
GECCO '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computationAir operations mission planning is a complex task, growing ever more complex as the number, variety, and interactivity of air assets increases. Mission planners are responsible for generating as close to optimal taskings of air assets to missions under ...
Comments