Real-Time Collision Avoidance in Dynamic Environments With a High-speed Skeleton Tracking and a Velocity Potential Field Method
Considering the coexistence of robot and human, robots must avoid colliding with human for safety.
Therefore, we develop a high-speed skeleton tracking method with deep learning-based detection and optical flow.
Furthermore, we also develop real-time collision avoidance path planning method dealing with dynamic human arm skeleton as obstacles based on velocity potential field.
We achieved tracking 6 joints of arms with 369 Hz, more than 5 times faster than the previous method.
We also verified effectiveness of our collision avoidance planning method in a simulation and confirmed that it works with over 10000 Hz.
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