@inproceedings{63bf9b668b92452abcbfce15954e08da,
title = "AG-DPSO: Landing Position Planning Method for Multi-node Deep Space Explorer",
abstract = "Explorer landing is a key stage in the deep space exploration mission for small celestial body. Traditional single-node deep space explorers are difficult to land on small celestial body with complex conditions. This paper analyzes the characteristics and difficulties of deep space exploration missions for small celestial body and proposes a new paradigm of multi-node deep space explorers, then analyzes the constraints of the multi-node explorer system and proposes a method for multi-node landing position selection named Adaptive Genetic Discrete Particle Swarm Optimization (AG-DPSO). AG-DPSO inherits GA{\textquoteright}s global search capability and PSO{\textquoteright}s fast convergence quality. Through adaptive genetic factors to control the mutation behavior of particle swarms, the landing position of multi-node deep space explorer can be obtained. Experiments demonstrate that the proposed approach is effective in dealing with the multi-node landing position planning issue.",
keywords = "AG-DPSO, Autonomous landing, Position plan",
author = "Yongquan Chen and Qingjie Zhao and Rui Xu",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Singapore Pte Ltd.; 5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020 ; Conference date: 25-12-2020 Through 27-12-2020",
year = "2021",
doi = "10.1007/978-981-16-2336-3_19",
language = "English",
isbn = "9789811623356",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "206--218",
editor = "Fuchun Sun and Huaping Liu and Bin Fang",
booktitle = "Cognitive Systems and Signal Processing - 5th International Conference, ICCSIP 2020, Revised Selected Papers",
address = "Germany",
}