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Vision-based obstacle avoidance robotic arm path planning based on a multi-level PPO framework
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DOI：10.1016/j.rineng.2025.107021.png)
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Abstract
En 中文
• Proposed a vision-based multi-level path planning framework for robotic arms. • Integrated YOLOv10 RGB-D perception with RRT and B-spline trajectory planning. • Combined perception, planning, and PPO control for closed-loop decision making. • Achieved up to 99.8% detection accuracy and high success rates in path planning. • Demonstrated robustness and adaptability in static and dynamic obstacle settings.
Keywords:
Robotic arm
Path planning
Reinforcement learning algorithm proximal policy optimization
Multi-level framework
RGB-D
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