Please use this identifier to cite or link to this item:
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.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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

Page view(s)

Updated on Mar 25, 2023

Download(s) 50

Updated on Mar 25, 2023

Google ScholarTM


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