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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
