This book examines the autonomous management of spacecraft, which uses modern control technologies such as artificial intelligence to establish a remote intelligent body on the spacecraft so that the spacecraft can complete its flight tasks by itself. Its goal is to accurately perceive its own state and external environment without relying on external information injection and control, or rely on external control as little as possible, make various appropriate decisions based on this information and user tasks, and be able to autonomously control spacecraft to complete various tasks.
- Divides the autonomous management level of spacecraft into two levels: - Basic autonomy to meet spacecraft health requirements, namely, autonomous health management, and autonomy of the advanced stage.
- Divides the implementation of spacecraft autonomous management into three aspects: - Autonomous health management of spacecraft – the spacecraft can monitor and sense its own state and can autonomously detect, isolate, and recover from faults. - Autonomous mission management – the spacecraft can directly receive the mission, formulate a reasonable plan according to the current state and working environment of the spacecraft, and convert the mission into a specific sequence of instructions. - Spacecraft autonomous data management – the spacecraft processes a large amount of raw data and extracts useful information and autonomously executes or changes flight tasks.
- The autonomous management model of the spacecraft is divided into two points: - Compatibility – the existing traditional control systems belong to the execution layer logic and are compatible with the existing systems. - Scalability – it adopts a layered structure, and each layer has different autonomous capabilities.
Dr Jianjun Zhang is a Professor at the China Academy of Space Technology. He is member of the Edge Computing Expert of the China Electronics Society, Chairman of the "Space (Aerospace) Information Technology" Professional Committee of China Electronics Society, and member of the Satellite Application Expert Group of China Aerospace Society. He is mainly engaged in research on satellite navigation system design, advanced spatial information system technology based on cognitive mechanism
Jing Li, Ph.D., is an Associate Professor at the School of Automation, Beijing Institute of Technology. Expert member of the “Space Information Technology” Youth Committee of China Electronics Society, the main research direction is robot environmental awareness, image detection and target tracking, multi-sensor information fusion. She has presided over more than 10 projects including the National Natural Science Youth Fund, the Postdoctoral Special Fund, the Key Laboratory of the Ministry of Education, and the Science and Technology Cooperation. She has published 25 academic papers (including 10 SCI papers and 15 EI papers) and the book "Image Detection and Object Tracking Technology", granted 7 national invention patents as the first author and the National Science and Technology Progress Award 2 (ranked 8th).
