Robot obstacle avoidance using reinforcement learning
Path planning is one of the essential parts of the autonomous robotic field and cars. There are many paths planning algorithms use to solve the static environment. However, path planning for a dynamic environment is challenging in this robotic field. Some traditional path planning strategies pre-def...
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sg-ntu-dr.10356-1585112023-07-07T18:56:22Z Robot obstacle avoidance using reinforcement learning Nang Yune Thitsar Hu Guoqiang School of Electrical and Electronic Engineering GQHu@ntu.edu.sg Engineering::Electrical and electronic engineering Path planning is one of the essential parts of the autonomous robotic field and cars. There are many paths planning algorithms use to solve the static environment. However, path planning for a dynamic environment is challenging in this robotic field. Some traditional path planning strategies pre-defined the robots' routes since the climate is already known. To face the dynamic environment, robots need to be more intelligent. Reinforcement learning is one of the machine learning techniques used to solve the dynamic environment and complex situations. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-06-03T04:24:57Z 2022-06-03T04:24:57Z 2022 Final Year Project (FYP) Nang Yune Thitsar (2022). Robot obstacle avoidance using reinforcement learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158511 https://hdl.handle.net/10356/158511 en P1021-202 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Nang Yune Thitsar Robot obstacle avoidance using reinforcement learning |
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Path planning is one of the essential parts of the autonomous robotic field and cars. There are many paths planning algorithms use to solve the static environment. However, path planning for a dynamic environment is challenging in this robotic field. Some traditional path planning strategies pre-defined the robots' routes since the climate is already known. To face the dynamic environment, robots need to be more intelligent. Reinforcement learning is one of the machine learning techniques used to solve the dynamic environment and complex situations. |
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Hu Guoqiang |
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Hu Guoqiang Nang Yune Thitsar |
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Final Year Project |
author |
Nang Yune Thitsar |
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Nang Yune Thitsar |
title |
Robot obstacle avoidance using reinforcement learning |
title_short |
Robot obstacle avoidance using reinforcement learning |
title_full |
Robot obstacle avoidance using reinforcement learning |
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Robot obstacle avoidance using reinforcement learning |
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Robot obstacle avoidance using reinforcement learning |
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robot obstacle avoidance using reinforcement learning |
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Nanyang Technological University |
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2022 |
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https://hdl.handle.net/10356/158511 |
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