Samuele Gigante
A Machine Learning-Based Approach for Traversability Analysis and Path Planning in Martian Rovers.
Rel. Fabrizio Stesina. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2023
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Abstract
Artificial intelligence is considered one of the major breakthroughs in data analysis and decision-making of the last decades. It is one of the scientific disciplines that have most attracted the attention of the public and the scientific community, with applications ranging from the medical field to cybersecurity. With the advent of Internet-of-Things and the increasing number of high-quality sensors present inside most of market-dominating smartphones, the need for a class of methods capable of extracting fundamental information from an unprecedented volume of data was, and still is, very high. Machine and Deep Learning methods are the main instruments used nowadays to perform such intricate analyses, allowing to establish relationships between input features and output predictions in a similar way to the human brain, without requiring extensive training of human operators to repeat the same analysis on new data.
Interplanetary exploration through autonomous rover systems is a perfect example of a scientifically challenging task that can greatly benefit from the application of Machine Learning algorithms within its workflow
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