Landing Trajectory Generation and Energy Optimization for Unmanned Lunar Mission

Author:

Islam Md. Shofiqul1ORCID,Mehedi Ibrahim M.12ORCID

Affiliation:

1. Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah 21589, Saudi Arabia

2. Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah 21589, Saudi Arabia

Abstract

The moon is recognized as an important destination for space science and exploration. To find a satisfactory answer for the mystery of the universe and to make use of the lunar resources for the welfare of human beings, several space agencies are planning manned and unmanned missions on the moon. As a result, the concept of lunar vehicles has begun with an advanced descent scheme to execute a precise and safe landing on the surface of the moon. On the contrary, the energy budget is an important issue for any space mission. To reduce the cost of a space mission, it is necessary to design the vehicle trajectory based on optimized energy resources. Fuel is the main energy in a space mission. Therefore, a fuel-optimized energy generation technique is focused on this research. The design of an algorithm that generates a real-time trajectory for the descent and landing of a lunar probe is critical to ensuring a successful lunar landing mission. A scheme of dual-step trajectory generation for lunar descent is also investigated in this paper. In the algorithm developing process, the thrust-to-mass ratio is considered as a principle variable. Algorithm design along with mathematical modeling and simulation results are described in detail. In addition, the proposed method for generating reference trajectory profiles is also analyzed for fuel consumption and robustness.

Funder

King Abdulaziz University

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Soft Landing Parameter Measurements for Candidate Navigation Trajectories Using Deep Learning and AI-Enabled Planetary Descent;Mathematical Problems in Engineering;2022-08-27

2. Solving the Lunar Lander Problem using Reinforcement Learning;2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS);2021-12-16

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