Abstract
Abstract
Human-robot interaction is crucial for the future of smart factories and new industrial systems. Safety in robotics has always been a top priority, with external sensors being studied to construct safety perception systems for robots. This paper proposes an obstacle avoidance strategy based on an efficient distance estimation method using a vision sensor to address the challenge of robot occlusion. The method fuses depth images with a predefined robot skeleton model to estimate robot pose in real time, and uses the optimized potential field model to achieve full-body collision avoidance. Comparative experiments validate the efficiency of the proposed method, which represents a significant contribution to enhancing human–robot interaction and safety in industrial settings.
Funder
Key R&D Program of China
Science and Technology Development Fund Project
National Natural Science Foundation of China
Natural Science Foundation of Hebei Province