Learning social norms through simulated crowd interaction

Autonomous Mobile robots are increasingly populating our human environments. A safe and efficient navigation system would be an essential capability that an autonomous mobile robot should be equipped with. This navigation system should be required to follow commonly accepted rules or adhere to socia...

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Main Author: Dinesdkumar Jayakumaran
Other Authors: Xiao Gaoxi
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/158151
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1581512023-07-07T19:24:05Z Learning social norms through simulated crowd interaction Dinesdkumar Jayakumaran Xiao Gaoxi School of Electrical and Electronic Engineering I2R-ASTAR Wan Kong Wah EGXXiao@ntu.edu.sg, kongwah@i2r.a-star.edu.sg Engineering::Electrical and electronic engineering Autonomous Mobile robots are increasingly populating our human environments. A safe and efficient navigation system would be an essential capability that an autonomous mobile robot should be equipped with. This navigation system should be required to follow commonly accepted rules or adhere to social norms Previous research has established that machine learning methods and simulation platforms are used to develop navigation system for autonomous robots. However, the previous research lacks some key aspects. Previous research that utilizes simulation software to collect data points, utilizes non-realistic environment settings or do not have a realistic crowd movement. Not only that, the crowd or human actors in the simulated environments are not depicting any realistic real-life scenarios. This FYP aims to investigate how can a mobile robot navigate in a crowded situation in a socially compliant manner. The three areas this final year project would be focusing on are 1. To create a realistic indoor crowd-simulation based on a simulator. 2. Utilize current state of the art navigation system and evaluate its effectiveness 3. Utilize deep reinforcement learning methods to train a model to allow a robot to navigate in a crowded situation. In this Final Year Project, we have developed a new realistic hospital ward environment in the gazebo simulator. We have also incorporated human actors who have similar behaviour to humans when encountered with an obstacle. These human actors have performed tasks that are like their real-life counterparts based on a survey done 21 healthcare professionals. We have also utilized the ROS navigation stack to evaluate its effectiveness in the hospital ward environment. We have also concluded that the ROS navigation stack is not able to deal with obstacles that ignore the robot. We have also identified that the robot tends to get stuck and forgoes its goal. To solve this, we implemented a Deep Q -Learning model. After 3000 episodes we were able to see some improvements. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-30T08:16:16Z 2022-05-30T08:16:16Z 2022 Final Year Project (FYP) Dinesdkumar Jayakumaran (2022). Learning social norms through simulated crowd interaction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158151 https://hdl.handle.net/10356/158151 en B3292-211 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Dinesdkumar Jayakumaran
Learning social norms through simulated crowd interaction
description Autonomous Mobile robots are increasingly populating our human environments. A safe and efficient navigation system would be an essential capability that an autonomous mobile robot should be equipped with. This navigation system should be required to follow commonly accepted rules or adhere to social norms Previous research has established that machine learning methods and simulation platforms are used to develop navigation system for autonomous robots. However, the previous research lacks some key aspects. Previous research that utilizes simulation software to collect data points, utilizes non-realistic environment settings or do not have a realistic crowd movement. Not only that, the crowd or human actors in the simulated environments are not depicting any realistic real-life scenarios. This FYP aims to investigate how can a mobile robot navigate in a crowded situation in a socially compliant manner. The three areas this final year project would be focusing on are 1. To create a realistic indoor crowd-simulation based on a simulator. 2. Utilize current state of the art navigation system and evaluate its effectiveness 3. Utilize deep reinforcement learning methods to train a model to allow a robot to navigate in a crowded situation. In this Final Year Project, we have developed a new realistic hospital ward environment in the gazebo simulator. We have also incorporated human actors who have similar behaviour to humans when encountered with an obstacle. These human actors have performed tasks that are like their real-life counterparts based on a survey done 21 healthcare professionals. We have also utilized the ROS navigation stack to evaluate its effectiveness in the hospital ward environment. We have also concluded that the ROS navigation stack is not able to deal with obstacles that ignore the robot. We have also identified that the robot tends to get stuck and forgoes its goal. To solve this, we implemented a Deep Q -Learning model. After 3000 episodes we were able to see some improvements.
author2 Xiao Gaoxi
author_facet Xiao Gaoxi
Dinesdkumar Jayakumaran
format Final Year Project
author Dinesdkumar Jayakumaran
author_sort Dinesdkumar Jayakumaran
title Learning social norms through simulated crowd interaction
title_short Learning social norms through simulated crowd interaction
title_full Learning social norms through simulated crowd interaction
title_fullStr Learning social norms through simulated crowd interaction
title_full_unstemmed Learning social norms through simulated crowd interaction
title_sort learning social norms through simulated crowd interaction
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/158151
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