Path planning and tracking of a network intelligent robot
Artificial intelligence (AI) has seen major improvements in the past decade, with much more applications of AI in various sectors, from board games to cybersecurity to engineering. In this report, we explore the use of AI in controlling a mobile robot to follow a given path, specifically using Re...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157334 |
| _version_ | 1826109462160605184 |
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| author | Tan, Yu Qin |
| author2 | Hu Guoqiang |
| author_facet | Hu Guoqiang Tan, Yu Qin |
| author_sort | Tan, Yu Qin |
| collection | NTU |
| description | Artificial intelligence (AI) has seen major improvements in the past decade, with much more
applications of AI in various sectors, from board games to cybersecurity to engineering. In this
report, we explore the use of AI in controlling a mobile robot to follow a given path, specifically
using Reinforcement Learning (RL).
A model of the mobile robot with inputs linear velocity and angular velocity, [v, ω] will be
used for this project. Deep Deterministic Policy Gradient (DDPG) will be used to control these
2 inputs to follow a path. Firstly, a simple trajectory will be given, which is in a straight positive
𝑥 direction. This can be achieved using a reward-based algorithm where a reward is given for
moving in the positive direction while a penalty is given for any deviation in the y-direction or
orientation. |
| first_indexed | 2024-10-01T02:18:37Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157334 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:18:37Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1573342023-07-07T19:11:40Z Path planning and tracking of a network intelligent robot Tan, Yu Qin Hu Guoqiang School of Electrical and Electronic Engineering GQHu@ntu.edu.sg Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Artificial intelligence (AI) has seen major improvements in the past decade, with much more applications of AI in various sectors, from board games to cybersecurity to engineering. In this report, we explore the use of AI in controlling a mobile robot to follow a given path, specifically using Reinforcement Learning (RL). A model of the mobile robot with inputs linear velocity and angular velocity, [v, ω] will be used for this project. Deep Deterministic Policy Gradient (DDPG) will be used to control these 2 inputs to follow a path. Firstly, a simple trajectory will be given, which is in a straight positive 𝑥 direction. This can be achieved using a reward-based algorithm where a reward is given for moving in the positive direction while a penalty is given for any deviation in the y-direction or orientation. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-12T07:48:46Z 2022-05-12T07:48:46Z 2022 Final Year Project (FYP) Tan, Y. Q. (2022). Path planning and tracking of a network intelligent robot. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157334 https://hdl.handle.net/10356/157334 en A1065-211 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Tan, Yu Qin Path planning and tracking of a network intelligent robot |
| title | Path planning and tracking of a network intelligent robot |
| title_full | Path planning and tracking of a network intelligent robot |
| title_fullStr | Path planning and tracking of a network intelligent robot |
| title_full_unstemmed | Path planning and tracking of a network intelligent robot |
| title_short | Path planning and tracking of a network intelligent robot |
| title_sort | path planning and tracking of a network intelligent robot |
| topic | Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics |
| url | https://hdl.handle.net/10356/157334 |
| work_keys_str_mv | AT tanyuqin pathplanningandtrackingofanetworkintelligentrobot |