研究目的
To design a visual servo control system combining model-based image segmentation and an Ant Colony Optimization algorithm for a 6-DOF robot manipulator to efficiently solve pick-and-place tasks by finding near-optimal paths and accurately manipulating objects.
研究成果
The proposed system effectively combines image processing, ACO algorithm, and kinematic modeling to achieve efficient pick-and-place tasks with high accuracy and robustness, demonstrating feasibility in both simulations and real-world applications, with potential for future enhancements in industrial automation.
研究不足
The study may have limitations in handling highly dynamic environments, scalability to larger numbers of objects, and real-time processing constraints. Optimization of parameters like pheromone evaporation rate and iteration numbers could be further explored.
1：Experimental Design and Method Selection:
The study uses a perceptual architecture with vision-based segmentation, ACO algorithm for path planning, and kinematic modeling. Methods include RGB to YCrCb color space transformation, K-means clustering, Sobel edge detection, median filtering, opening operations, labeling-based segmentation, and ACO for optimization.
2：Sample Selection and Data Sources:
Images captured by two cameras in an experimental setup with 24 objects (balls and cubes of different colors) and 8 category boxes.
3：List of Experimental Equipment and Materials:
Cameras for image capture, a 6-DOF robot manipulator, and computational tools for simulations.
4：Experimental Procedures and Operational Workflow:
Steps involve image segmentation to extract object attributes, ACO algorithm initialization and iteration for path selection, kinematic model application for robot control, and validation through software simulations and real-world implementations.
5：Data Analysis Methods:
Performance evaluated based on path length, success rate in path finding, and task completion time using statistical analysis from repeated experiments.
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