Enrico Sutera
Deep Reinforcement Learning and Ultra-Wideband for autonomous navigation in service robotic applications.
Rel. Marcello Chiaberge. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2019
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Abstract
Autonomous navigation for service robotics is one the greatest challenges and there's a huge effort from scientific community. This work is born at PIC4SeR (PoliTo Interdepartmental Centre for Service Robotics) with the idea of facing the aforementioned challenge merging rediscovered and promising technologies and techniques: Deep Reinforcement Learning and Ultra-Wideband technology. Over few past years the world has seen a huge advance in the field of Artificial Intelligence, especially thanks to Machine Learning techniques. The latter include a branch called Deep Reinforcement Learning (DRL) that involves the training of Artificial Neural Network (ANN) from experience, i.e. without the need of huge datasets.
Here DRL has been used to train an agent able to perform goal reaching and obstacle avoidance
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