AI-based multiple obstacle detection and avoidance for a 6DOF robot manipulator

With the rapid advancement of Industry 4.0 in the industrial sector, robotic arms have become crucial in warehouse logistics. Due to the complex and dynamic changing warehouse environments, it is essential for robotic arms to accurately avoid obstacles while working together. Currently, most obst...

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Main Author: Wang, ChenYang
Other Authors: Hu Guoqiang
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/181670
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1816702024-12-13T15:47:38Z AI-based multiple obstacle detection and avoidance for a 6DOF robot manipulator Wang, ChenYang Hu Guoqiang School of Electrical and Electronic Engineering GQHu@ntu.edu.sg Engineering With the rapid advancement of Industry 4.0 in the industrial sector, robotic arms have become crucial in warehouse logistics. Due to the complex and dynamic changing warehouse environments, it is essential for robotic arms to accurately avoid obstacles while working together. Currently, most obstacle avoidance algorithms use pre-trained methods like RRT and A*, but these algorithms consume a lot of computing resources in continuous spaces and when dealing with complex obstacles. In contrast, reinforcement learning algorithms do not require a complete pre-built environmental model and can learn strategies on their own through interaction with the environment. Additionally, the YOLO algorithm is used for fast obstacle detection and, when combined with depth cameras, can accurately determine the specific positions of obstacles. Although LiDAR algorithms offer higher precision, they require more computing resources and are more costly. The YOLO algorithm can balance accuracy and speed in warehouse obstacle avoidance settings. This study aims to improve the autonomous obstacle avoidance capabilities of robotic arms in complex warehouse environments. We combined deep sensors with a retrained YOLO algorithm to accurately detect the positions of multiple obstacles, achieving a high detection accuracy a high confidence level. We used the PPO reinforcement learning algorithm to train the robotic arm for obstacle avoidance, enabling it to effectively navigate around obstacles in complex settings. This research provides an effective solution for autonomous navigation of robotic arms in static complex environments. Future research will further extend to obstacle detection and avoidance in dynamic complex environments. Master's degree 2024-12-12T11:50:56Z 2024-12-12T11:50:56Z 2024 Thesis-Master by Coursework Wang, C. (2024). AI-based multiple obstacle detection and avoidance for a 6DOF robot manipulator. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181670 https://hdl.handle.net/10356/181670 en 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
spellingShingle Engineering
Wang, ChenYang
AI-based multiple obstacle detection and avoidance for a 6DOF robot manipulator
description With the rapid advancement of Industry 4.0 in the industrial sector, robotic arms have become crucial in warehouse logistics. Due to the complex and dynamic changing warehouse environments, it is essential for robotic arms to accurately avoid obstacles while working together. Currently, most obstacle avoidance algorithms use pre-trained methods like RRT and A*, but these algorithms consume a lot of computing resources in continuous spaces and when dealing with complex obstacles. In contrast, reinforcement learning algorithms do not require a complete pre-built environmental model and can learn strategies on their own through interaction with the environment. Additionally, the YOLO algorithm is used for fast obstacle detection and, when combined with depth cameras, can accurately determine the specific positions of obstacles. Although LiDAR algorithms offer higher precision, they require more computing resources and are more costly. The YOLO algorithm can balance accuracy and speed in warehouse obstacle avoidance settings. This study aims to improve the autonomous obstacle avoidance capabilities of robotic arms in complex warehouse environments. We combined deep sensors with a retrained YOLO algorithm to accurately detect the positions of multiple obstacles, achieving a high detection accuracy a high confidence level. We used the PPO reinforcement learning algorithm to train the robotic arm for obstacle avoidance, enabling it to effectively navigate around obstacles in complex settings. This research provides an effective solution for autonomous navigation of robotic arms in static complex environments. Future research will further extend to obstacle detection and avoidance in dynamic complex environments.
author2 Hu Guoqiang
author_facet Hu Guoqiang
Wang, ChenYang
format Thesis-Master by Coursework
author Wang, ChenYang
author_sort Wang, ChenYang
title AI-based multiple obstacle detection and avoidance for a 6DOF robot manipulator
title_short AI-based multiple obstacle detection and avoidance for a 6DOF robot manipulator
title_full AI-based multiple obstacle detection and avoidance for a 6DOF robot manipulator
title_fullStr AI-based multiple obstacle detection and avoidance for a 6DOF robot manipulator
title_full_unstemmed AI-based multiple obstacle detection and avoidance for a 6DOF robot manipulator
title_sort ai-based multiple obstacle detection and avoidance for a 6dof robot manipulator
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/181670
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