# Using Artificial Intelligence for Space Challenges: A Survey
## 4 State of the Art
### Works on Space Exploration  
Artificial intelligence will enhance space exploration in the near future [70]. Indeed, intelligent tools that analyze data and predict events will be used in every aspect of space settlement. Automation and robotics have always played a significant role in space exploration [71], opening new frontiers and improvements since the beginning. The exploitation of AI contributes to creating independent and intelligent robots able to learn while in orbit. In addition, the management of space traffic and the assistance of ground operators can be supported by AI techniques. In [72], the authors trained predictive models through Multiple Regression Analysis. Their study aimed to forecast collision avoidance maneuvers and better manage space traffic. Improving the autonomous functions of space robots, spacecraft, and equipment is a need that can be achieved through AI systems. The authors of [73] survey the impact of AI on the field of spacecraft guidance dynamics and control. They focus on evolutionary optimization, tree searches, and machine learning using deep learning and reinforcement learning, analyzing interesting use cases where these algorithms improved the performances of guidance and control of well-known problems [74]. The authors of [75] consider the advantages of a multi-agent system in terms of robustness and reliability of space operations. Furthermore, the intelligent use of multi-robots integrated with a human-to-loop approach mission costs and human efforts. The study in [76] provides a methodology for designing and controlling elastic, computing self-organization for AI space exploration. The use of swarm cooperative rovers is based on a set of shared information supporting the mission planned to reach a common target [77]. In [75], a cooperative, coevolutionary algorithm able to train a multi-robot system is presented. Any space exploration scenario presents several challenges that can be met jointly by both humans and swarm robots. The authors of [36] explore a scenario based on Mars, where human scientists train robots and multi-robot systems to perform primary tasks. A multi-agent deep deterministic policy gradient algorithm is exploited. In [78], a decentralized multi-robot system operation technique is presented. The approach is evaluated in a rough terrain environment artificially generated to perform a collaborative object transportation mission. The technique is based on a fuzzy genetic system exploiting a genetic algorithm to optimize a fuzzy inference system. AI techniques play an important role in astrophysics and astronomy. AI searches space in planetary studies and conducts real-time monitoring of instruments and investigations of stellar clusters and non-stellar components of the Milky Way (for example, see [79]). Notably, as suggested in [80], deep learning and neural networks can automate the detection of astronomical bodies and sustain future exploration missions. The authors of [81] introduce simulator-based methods using artificial intelligence to obtain images during deep-space journeys in NASA Starlight and Breakthrough Starshot using a Grow When Required Network, an unsupervised technique. In addition, to better understand Mars, several Mars images have been collected and need to be analyzed. In [82], semi-supervised contrastive learning is exploited to classify Mars rover images. Regarding the Jovian system, the authors of [83] proposed an approach based on reinforcement learning for the solution of the J2-perturbed Lambert problem in the field of space trajectory design [84] and to overcome errors due to incomplete information about pursuit-evasion missions. Furthermore, ANNs have been exploited to model accurate and efficient maps of Jovian-moon gravity-assisted transfers [85; 86; 87]. Convolutional neural networks coupled with a recurrent neural network are exploited to create a vision-based navigation system for a pinpoint landing on the Moon [88].