Robot Navigation in Cluttered Environments with Deep Reinforcement Learning

<p>The application of robotics in cluttered and dynamic environments provides a wealth of challenges. This thesis proposes a deep reinforcement learning based system that determines collision free navigation robot velocities directly from a sequence of depth images and a desired direction of travel. The system is designed such that a real robot could be placed in an unmapped, cluttered environment and be able to navigate in a desired direction with no prior knowledge. Deep Q-learning, coupled with the innovations of double Q-learning and dueling Q-networks, is applied. Two modifications of this architecture are presented to incorporate direction heading information that the reinforcement learning agent can utilize to learn how to navigate to target locations while avoiding obstacles. The performance of the these two extensions of the D3QN architecture are evaluated in simulation in simple and complex environments with a variety of common obstacles. Results show that both modifications enable the agent to successfully navigate to target locations, reaching 88% and 67% of goals in a cluttered environment, respectively.</p>

Name thesis:degree_name

MS in Electrical Engineering

Discipline thesis:degree_discipline

Electrical Engineering

Year dc:date.available

2019

Author dc:creator

• Weideman, Ryan

Contributors dc:contributor

• John Seng

• robotics
• reinforcement learning
• navigation
• machine learning

Identifier

10.15368/theses.2019.31

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-3413