Print Email Facebook Twitter Toward Reliable Robot Navigation Using Deep Reinforcement Learning Title Toward Reliable Robot Navigation Using Deep Reinforcement Learning Author van Rietbergen, Tomas (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Venkatesha Prasad, Ranga Rao (mentor) Amjad Yousef Majid, Amjad (graduation committee) Babuska, R. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Embedded Systems Date 2022-11-07 Abstract Reliable indoor navigation in the presence of dynamic obstacles is an essential capability for mobile robot deployment. Previous work on robot navigation focuses on expanding the network structure and hardware setup leading to more complex and costly systems. The accompanying physical demonstrations are often limited to slow-moving agents and simplistic obstacle configurations. In this thesis, we develop an end-to-end navigation system with a focus on real-world transferability and produce a low-cost and customizable robot platform. Instead of expanding the network structure, we rely on external capabilities such as backward motion, frame stacking, and behavioral reward design to improve performance while preserving transferability. By convention, these methods have been largely disregarded in previous works on deep reinforcement learning (DRL) for unmanned ground vehicle (UGV) navigation. We analyze the effect on performance in simulation of different off-policy algorithms with hyperparameter and reward function configurations. Experimental results show that our agent can achieve state-of-the-art performance in challenging and unseen simulated environments. In addition, physical robot demonstrations show that our system is capable of dealing with fast-moving and unpredictable agents in a real-world environment. Subject Deep Reinforcement LearningRobot Navigationphysical experimentDDPGTD3DQN To reference this document use: http://resolver.tudelft.nl/uuid:8dda87ed-5acd-44fc-9a31-e8a60b20f43b Bibliographical note https://www.youtube.com/user/tomasvr1 Youtube channel with demonstration videos related to thesis project. Part of collection Student theses Document type master thesis Rights © 2022 Tomas van Rietbergen Files PDF Tomas_Thesis_Final.pdf 9.09 MB Close viewer /islandora/object/uuid:8dda87ed-5acd-44fc-9a31-e8a60b20f43b/datastream/OBJ/view