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EXPLORATION OF MARS THROUGH AN AUTONOMOUS AND MACHINE LEARNING ENABLED CONSTELLATION OF DRONES

机译:通过一种自主和机器学习的火星探索使能无人机星座

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Other than the Moon, Mars has held a special position in human fascination. Although it is not the closest planet to Earth, it closely resembles the Earth and excites our imagination, to explore its planetary surface to increase our understanding of its environment and mineralogy. Exploration of Mars has been the major driving force and destination, of space exploration for past few decades. Reaching to Mars has been a challenge in itself, let alone the development of a system to explore its surface. Although Curiosity and Opportunity have been on Mars for more than a combined 6000 sols, not much of the surface has been mapped. Many orbiters have also been placed into Mars's orbit, but the prospect of mapping the Martian surface through rovers, provides more accurate data about the mineralogy of the soil, however, robotic exploration is a slower process when compared to aerial surveillance. This paper presents a sustainable approach to map the surface of Mars through a Machine Learning enabled and Autonomous, Constellation of Drones, flying in a synchronized manner over the Martian soil. The mission architecture proposed through the paper has been designed to maximize the scientific data from the Martian surface. Various trade studies will be included at the architectural and system levels to demonstrate the compliance of the science instruments for a complete optimization. A detailed description of the scientific approach to establishing communication among the drones will be included. Detailed surface experiments and communication plans for the constellation of drones will also be showcased. Comprehensive tables and graphs will be given to illustrate the compliance of the functionality of the constellation of drone, in the harsh environment of Mars. Tables will also be given to depict the amount of time that will pass at each mode of travel and more importantly, some idea on the cost in terms of energy, as well as money, will be discussed within today's context. E
机译:除月亮之外,火星在人类迷恋中担任特殊职位。虽然它不是地球上最近的行星,但它与地球相似,兴奋地兴奋,探索其行星表面,以增加我们对环境和矿物的理解。探索火星是过去几十年来探索的主要推动力和目的地。到达火星本身就是一个挑战,更不用说发展系统来探索其表面。虽然在火星上的好奇心和机会超过了6000个溶剂,但没有大部分地面已经映射。许多轨道也被放入Mars的轨道中,但通过Rover映射Martian表面的前景,提供了关于土壤矿物学的更准确的数据,然而,与空中监测相比,机器人勘探是一种较慢的过程。本文介绍了一种可持续的方法,可以通过机器学习映射火星表面,使无人机星座的星座,以同步的方式在火星土壤中飞行。通过本文提出的任务架构旨在最大限度地提高火星表面的科学数据。各种贸易研究将被列入建筑和系统水平,以证明科学仪器符合完整优化的遵守情况。将包括科学方法在无人机之间建立沟通的详细描述。还将展示细化的细节表面实验和通信计划。将综合表格和图表说明火星恶劣环境中无人机星座的功能的依从性。还将描述将在每种旅行模式下传递的时间,更重要的是,将在当今的背景下讨论能源和金钱方面的成本的一些想法。 E.

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