{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T11:45:40Z","timestamp":1774352740646,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This paper improves the timeliness of satellite mission planning to cope with the rapid response to changes. In this paper, satellite mission planning is investigated. Firstly, the satellite dynamics model and mission planning model are established, and an improved Monte Carlo tree (Improved-MCTS) algorithm is proposed, which utilizes the Monte Carlo tree search in combination with the state uncertainty network (State-UN) to reduce the time of exploring the nodes (At the MCTS selection stage, the exploration of nodes specifically refers to the algorithm needing to decide whether to choose nodes that have already been visited (exploitation) or nodes that have not been visited yet (exploration)). The results show that this algorithm performs better in terms of profit (in this paper, the observation task is given a weight of 0\u20131, and each planned task will receive a profit; that is, a profit will be assigned at the initial moment) and convergence speed compared to the ant colony algorithm (ACO) and the asynchronous advantage actor critic (A3C).<\/jats:p>","DOI":"10.3390\/sym16081039","type":"journal-article","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T10:54:54Z","timestamp":1723546494000},"page":"1039","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Satellite Autonomous Mission Planning Based on Improved Monte Carlo Tree Search"],"prefix":"10.3390","volume":"16","author":[{"given":"Zichao","family":"Li","sequence":"first","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"}]},{"given":"You","family":"Li","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"}]},{"given":"Rongzheng","family":"Luo","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"key":"ref_1","unstructured":"Beaumet, G., Verfaillie, G., and Charmeau, M.C. (, January February). Autonomous planning for an agile earth-observing satellite. Proceedings of the ISAIRAS, Los Angeles, CA, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1287\/mnsc.46.1.148.15134","article-title":"Three Scheduling Algorithms Applied to the Earth Observing Systems Domain","volume":"46","author":"Wolfe","year":"2000","journal-title":"Manag. Sci."},{"key":"ref_3","unstructured":"Lema\u00eetre, M., and Verfaillie, G. (, January July). Daily management of an earth observation satellite. Proceedings of the Comparison of ILOG International Users Meeting, Paris, France."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S1270-9638(02)01173-2","article-title":"Selecting and scheduling observations of agile satellites","volume":"6","author":"Verfaillie","year":"2002","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_5","unstructured":"Habet, D., and Vasquez, M. (2003, January 25\u201328). Saturated and Consistent Neighborhood for Selecting and Scheduling Photographs of Agile Earth Observing Satellite. Proceedings of the Fifth Metaheuristics International Conference, Kyoto, Japan."},{"key":"ref_6","unstructured":"Dilkina, B., and Havens, B. (2005). Agile Satellite Scheduling via Permutation Search with Constraint Propagation, Actenum Corporation."},{"key":"ref_7","first-page":"51","article-title":"Mission Planning and Action Planning for Agile Earth-Observing Satellite with Genetic Algorithm","volume":"20","author":"Sun","year":"2013","journal-title":"J. Harbin Inst. Technol. New Ser."},{"key":"ref_8","first-page":"53","article-title":"An algorithm of cooperative multiple satellites mission planning based on multi-agent reinforcement learning","volume":"33","author":"Wang","year":"2002","journal-title":"J. Natl. Univ. Def. Technol. China"},{"key":"ref_9","unstructured":"Huang, H., Sun, C.Y., and Hu, J.X. (2020, January 23\u201325). Optimization design of response satellite deployment for regional target emergency observation. Proceedings of the 2020 International Conference on Guidance on Advances in Guidance, Navigation and Control, Tianjin, China."},{"key":"ref_10","first-page":"928","article-title":"Method of agile imaging satellites autonomous task planning","volume":"22","author":"Liu","year":"2016","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1016\/j.asr.2017.07.026","article-title":"An anytime branch and bound algorithm for agile earth observation satellite onboard scheduling","volume":"60","author":"Chu","year":"2017","journal-title":"Adv. Space Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"150","DOI":"10.3788\/OPE.20182601.0150","article-title":"Optimize-by-priority on-orbit task real-time planning for agile imaging satellite","volume":"26","author":"Miao","year":"2018","journal-title":"Opt. Precis. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.ast.2017.11.009","article-title":"Onboard mission planning for agile satellite using modified mixed-integer linear programming","volume":"72","author":"She","year":"2018","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3478","DOI":"10.1016\/j.asr.2022.08.016","article-title":"Deep reinforcement learning based autonomous mission planning method for high and low orbit multiple agile earth observing satellites","volume":"70","author":"Wang","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1016\/j.cja.2018.12.018","article-title":"Online scheduling of image satellites based on neural networks and deep reinforcement learning","volume":"32","author":"Wang","year":"2019","journal-title":"Chin. J. Aeronaut."},{"key":"ref_16","unstructured":"Zhang, R. (1998). Satellite Orbital Attitude Dynamics and Control, Beijing University of Aeronautics and Astronautics Press."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"0063","DOI":"10.34133\/space.0063","article-title":"Models and Strategies for J2-Perturbed Orbital Pursuit Evasion Games","volume":"3","author":"Han","year":"2023","journal-title":"Space Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.ast.2015.10.023","article-title":"Coplanar ground-track adjustment using time difference\u2014ScienceDirect","volume":"48","author":"Zhang","year":"2016","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_19","first-page":"1534","article-title":"Attitude coordination control for flexible spacecraft formation flying with guaranteed performance bounds","volume":"59","author":"Xiao","year":"2023","journal-title":"IEEE Trans. Aerosp Electron. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"0086","DOI":"10.34133\/space.0086","article-title":"Orbital Interception Pursuit Strategy for Random Evasion Using Deep Reinforcement Learning","volume":"3","author":"Jiang","year":"2023","journal-title":"Space Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1940009","DOI":"10.1142\/S0217595919400098","article-title":"Simulation-Based Algorithms for Markov Decision Processes: Monte Carlo Tree Search from AlphaGo to AlphaZero","volume":"36","author":"Fu","year":"2019","journal-title":"Asia Pac. J. Oper. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Petschnigg, C., Spitzner, M., Weitzendorf, L., and Pilz, J. (2021). From a Point Cloud to a Simulation Model Bayesian Segmentation and Entropy Based Uncertainty Estimation for 3D Modelling. Entropy, 23.","DOI":"10.3390\/e23030301"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/8\/1039\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:36:02Z","timestamp":1760110562000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/8\/1039"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,13]]},"references-count":22,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["sym16081039"],"URL":"https:\/\/doi.org\/10.3390\/sym16081039","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,13]]}}}