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A 3D vision-guided adaptive trajectory correction system for robot offline programming
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AI
DOI：10.1088/2631-8695/ae3277.png)
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Abstract
En 中文
Non-standard workpieces and geometric deviations between CAD models and physical parts often cause offline-generated robot trajectories to fail to meet precision requirements. To address this challenge, we present a 3D vision-guided adaptive trajectory correction system that fuses global CAD priors with local point cloud refinements. The perception stage introduces a region-weighted Point Pair Feature (PPF) method with visibility-aware preprocessing and a two-stage verification strategy, enhancing matching accuracy in task-critical regions while maintaining robustness to occlusion. In the trajectory generation stage, a lightweight local correction module detects and refines deviated trajectory segments through direction-guided point cloud projection and Catmull-Rom interpolation, achieving smooth and precise path adjustments without full replanning. Experimental results on rigid workpieces with induced defects show a 100% pose estimation success rate in the evaluated scenarios, an average 41% reduction in Region-of-Interest Root Mean Square Error (ROI-RMSE) compared to baseline PPF, and sub-millimeter trajectory conformity with millisecond-level latency. The proposed framework maintains compatibility with standard offline programming workflows, offering a practical and scalable solution for high-precision robotic guidance in complex industrial environments.
Keywords:
robot path planning
pose estimation
trajectory correction
3D vision
CAD/CAM
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