Real-Time Collision Avoidance in Dynamic Environmentswith High-speed Skeleton Tracking and Velocity Potential Field Method

Abstract

Considering the coexistence of robot and human, robots must avoid colliding with human for safety. Therefore, we developed high-speed skeleton tracking method with deep learning-based detection and optical flow. Furthermore, we also developed 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 380[Hz], more than 5 times faster than the previous method. We also verified effectiveness of our collision avoidance planning method in simulation and confirmed that it works with under 100 microseconds.