Cosimo Bromo
Reinforcement Learning Based Strategic Exploration Algorithm for UAVs Fleets.
Rel. Giorgio Guglieri, Simone Godio. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022
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Abstract
Nowadays, autonomous navigation systems are becoming increasingly pervasive in everyday life and work. Unmanned Aircraft Systems (UASs) have been developed in the recent years, conquering different market segments and gaining popularity for their versatility and usefulness. Besides the economic benefits arising from their employments, ranging from crops monitoring in agriculture to fast parcel deliveries, their greatest incentive is the feasibility in hazardous and high risk operations. Their rapid growth is mainly associated to the quick development of algorithms and strategies for autonomous navigation and task execution, involving both traditional approaches and applications of artificial intelligence (AI) algorithms. One of the most challenging but rewarding field of study is the coordinated behavior of a number agents, collaborating to carry out the same high level task as well as distinguished low level objectives.
Employment of fleets of Unmanned Aerial Vehicles (UAVs) may be particularly fruitful especially for time-sensitive operations, in which battery autonomy and time minimization are the most stringent requirements
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