AI Research Analysis
Research on an Autonomous Recognition and Grasping Manipulator System Based on YOLOv8
This paper presents an intelligent robotic arm system leveraging YOLOv8 to tackle perception delays and low target recognition accuracy in complex battlefield environments. By integrating six-axis robotic arm kinematics with deep-learning visual detection, we develop a cohesive 'perception-decision-execution' framework. Experimental results demonstrate high target recognition accuracy and grasping success rates in complex settings, offering a viable solution for intelligent equipment development in military and industrial applications.
Key Impact Metrics
The study highlights significant advancements in autonomous robotic capabilities, showcasing practical improvements:
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YOLOv8n Detection Accuracy
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Average Grasping Success Rate
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Reduced Perception Delay
Deep Analysis & Enterprise Applications
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The system utilizes the YOLOv8n lightweight object detection network, achieving a 99.7% detection accuracy in real-time. This model was chosen for its balance of efficiency and accuracy, critical for battlefield environments. The specialized dataset, augmented with image processing techniques, ensures robustness against varying lighting and angles.
Depth information for grasping is obtained through the SGBM stereo matching algorithm, integrated with the robotic arm's kinematic control. This allows for precise three-dimensional reconstruction of target positions, translating pixel coordinates to the camera and then to the robot arm's base coordinate system for accurate manipulation.
A kinematic model of the robotic arm, simulated in MATLAB, enables workspace analysis and autonomous trajectory planning. The Python-based control system facilitates autonomous recognition and grasping, ensuring the end-effector's motion is precisely aligned with the detected target's pose.
99.7%
YOLOv8n Detection Accuracy
Enterprise Process Flow: Autonomous Grasping
Real-time Image Capture
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YOLOv8 Object Detection
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SGBM Depth Calculation
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3D Coordinate Transformation
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Robotic Arm Motion Planning
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Grasping Execution
| Model | Precision | Recall | mAP@50 | mAP@50-95 |
|---|---|---|---|---|
| YOLOv5un | 0.986 | 1 | 0.995 | 0.977 |
| YOLOv8n | 0.997 | 1 | 0.995 | 0.995 |
| YOLOv11n | 0.995 | 1 | 0.995 | 0.977 |
Case Study: Battlefield Readiness - Explosive Handling
Description: The system was tested in complex battlefield scenarios, specifically for handling various military explosive simulation models.
Challenge: Low target recognition accuracy and perception delays in unstructured, dynamic environments.
Solution: Integration of YOLOv8 for robust object detection and binocular vision with SGBM for precise 3D positioning, coupled with a six-axis robotic arm with autonomous trajectory planning.
Outcome: Achieved high target recognition accuracy and an average grasping success rate of 68.3%, demonstrating viability for intelligent equipment in military applications. Peak success rate of 100% for single grasps.
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