Imaging Satellites Task Planning
De Gruyter (Verlag)
978-3-11-158466-9 (ISBN)
- Proposes a novel bi-level framework for complex combinatorial optimization problems
- Integrates deterministic algorithms and reinforcement learning for planning satellite tasks
- Applies orbiting satellite constellations in real-world scenarios
The continuous enhancement of platforms and payloads have enabled imaging satellites to obtain greater societal benefits, while to bring challenges to imaging satellite task planning: refinement of comprehensive control, normalization of quick response, and complication of constraints. It is precisely because of the aforementioned changes and requirements, the contradiction between algorithm versatility and efficiency, between solution efficiency and accuracy are becoming increasingly acute. In order to alleviate these two pairs of contradictions, this book conducts research on imaging satellite task planning technology integrating with operations research and reinforcement learning. Preliminary research on the design of imaging satellite task planning system, bi-level optimization model, and learning-based combinatorial optimization algorithms are conducted. The effectiveness of the proposed method is verified in real-world task planning scenarios of "SuperView-1" constellation. In other combinatorial optimization problems with complex constraints, the methodology proposed in this book has enormous advantages and potential. We aspire to stimulate the interest of readers in researching related scientific issues through this book.
Yongming He received the B.S. degree in Logistics Engineering from Chang’an University, China, in 2014, and the M.S and PhD degrees in Management Science and Engineering at National University of Defense Technology (NUDT), China, in 2016 and 2021, respectively. He is also a visiting Ph.D. student at University of Alberta, Edmonton, AB, Canada, from Nov. 2018 to Nov. 2019. Now, he is an associate professor at College of Systems Engineering of NUDT. Dr. He has 12 years of research experience and presently working on the field of intelligent task planning, optimization, and scheduling, and receives 1 first prize in Natural Science from the China Simulation Federation and 2 second prize in Natural Science from Hunan Province, and published over 20 papers; Aut2horized 12 Chinese patents.
Yingwu Chen received the B.S. degree in automation, the M.S. degree in system engineering, and the Ph.D. degree in engineering from NUDT, Changsha, China, in 1984, 1987, and 1994, respectively. He was a Lecturer from 1989 to 1994, and an associate professor from 1994 to 1999 at NUDT. From 1999 to 2017, he was a professor and the director of the department of Management Science and Engineering, College of Information Systems and Management, NUDT. Now, he has been a distinguished professor and doctoral supervisor of College of Systems Engineering, NUDT, a member of the 14th National Committee of the Chinese People's Political Consultative Conference. Prof. Chen has been engaged in research on satellite intelligent task planning and complex system decision analysis for over 20 years, and has authored more than 70 research publications, published 7 textbooks or monographs, and won 2 first prizes and 5 second prizes of Provincial Science and Technology Award.
| Erscheinungsdatum | 18.04.2025 |
|---|---|
| Reihe/Serie | De Gruyter STEM |
| Zusatzinfo | 5 b/w and 52 col. ill., 45 b/w tbl. |
| Verlagsort | Berlin/Boston |
| Sprache | englisch |
| Maße | 170 x 240 mm |
| Gewicht | 410 g |
| Einbandart | Paperback |
| Themenwelt | Technik ► Luft- / Raumfahrttechnik |
| Technik ► Maschinenbau | |
| Schlagworte | combinatorial optimization • Deterministic algorithm • imaging satellite • Reinforcement Learning • task planning |
| ISBN-10 | 3-11-158466-6 / 3111584666 |
| ISBN-13 | 978-3-11-158466-9 / 9783111584669 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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