Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139736
Title: Collision avoidance for automated guided vehicles using deep reinforcement learning
Authors: Qin, Yifan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Project: A1237-191
Abstract: It is crucial yet challenging to develop an efficient collision avoidance policy for robots. While centralized collision avoidance methods for multi-robot systems exist and they are often more accurate and error-free, decentralized methods have the potential to reduce the prohibitive computation where each robot generates paths without observing other robots’ states. As the first step towards a decentralized multi-robot collision avoidance system, this project aims to implement Deep Reinforcement Learning in the collision avoidance simulation of a single robot. The robot scans the environment around it and is supposed to find its way in a pre- designed map with multiple obstacles and branches. Several algorithms are tested and discussed in this project including Q Learning, SARSA, Deep Q Network (DQN), Policy Gradient (PG), Actor Critic, Deep Determinist Policy Gradient (DDPG), Distributed Proximal Policy Optimization (DPPO). Thorough comparisons between DQN, DDPG and DPPO are presented in this project.
URI: https://hdl.handle.net/10356/139736
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP_Report_QINYifan.pdf
  Restricted Access
Final Year Project Report10.42 MBAdobe PDFView/Open

Page view(s)

275
Updated on Jan 26, 2023

Download(s) 50

32
Updated on Jan 26, 2023

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.