Скачать или смотреть Obstacle Avoidance Simulation of Robotic Arm Based on Cyber-Physical Integration Technology [Final3]

2022 / 09 (The final Chapter)

The purpose of this study is to establish a process architecture based on the concept of Cyber-Physical integration, which can be applied to support ROS-related robotic arms. We verify the feasibility of this architecture through a case, and use it as a basic model to provide other researchers in the future for extension. Due to problems with the gripper of the robotic arm, the captured object is not shown in the video, but the grasping function is still included.
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First,we use the object detection method to identify the obstacle position, and realize the obstacle simulation in the virtual environment through data conversion (such as the lower and lower left corner windows in the video)
Second, combine the obstacle avoidance algorithm to plan the path of the robotic arm in the simulation software (such as the window in the lower right corner of the video)
Third, if it is judged that the robotic arm has no collision, the planning result is given to the real robot arm through signal transmission to complete the loading and unloading task (such as the upper and upper left corner windows in the video)
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Test1
In the absence of obstacles, the robotic arm completes the task in a normal path.
Test2
The obstacle is far away from the position of the robotic arm, and the path is calculated and verified through algorithms and simulation software, so that the real robot arm can avoid obstacles to complete the pick-and-place task.
Test3
The obstacle is close to the position of the robotic arm, and the path is calculated and verified through algorithms and simulation software, so that the real robot arm can avoid obstacles to complete the pick-and-place task.
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Operating system: Ubuntu18.04
System framework: ROS
Object detection: Yolov4
Obstacle Avoidance Algorithm: Artificial Potential Field Method
Simulation software: CoppeliaSim
Visualization software: RVIZ
Calculation tool: Matlab
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Paper reference link : https://hdl.handle.net/11296/44ba27