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Tesi etd-11142018-230013


Thesis type
Tesi di laurea magistrale
Author
CORDELLA, GIOVANNI MARCO
URN
etd-11142018-230013
Title
Continuous Control of UGV for Mapless Navigation: a Virtual-to-Real Deep Reinforcement Learning Approach
Struttura
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA ROBOTICA E DELL'AUTOMAZIONE
Commissione
relatore Prof.ssa Pallottino, Lucia
relatore Ing. Franchi, Michele
Parole chiave
  • deep reinforcement learning
  • Artificial Intelligence
  • Machine learning
  • autonomus robot
  • continuous action space
  • Actor-Critic
Data inizio appello
10/12/2018;
Consultabilità
completa
Riassunto analitico
The autonomous mobile robot must be able to adapt its skills in order to react adequately in complex and dynamic environments. Different approaches can be used to handle these kinds of applications: map-based navigation or map-less navigation. This work aims to prove that robot can goes towards target position, detecting and avoiding obstacles based on low dimensional data directly from sensors without the knowledge of the environment map. This goal is achieved using Deep Reinforcement Learning approach by a virtual-to-real strategy; thus, networks are trained in simulations before being inferred in real world. DRL method is Actor-Critic method and the training process is the Deterministic Policy Gradient update process.
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