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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Format: | Thesis-Master by Coursework |
Language: | English |
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Nanyang Technological University
2024
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Online Access: | https://hdl.handle.net/10356/181670 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | 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. |
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