Safe Multimodal Communication in Human-Robot Collaboration
This work addresses safety and efficiency in human-robot collaboration for industrial settings, though it appears incremental as it builds on existing multimodal and safety concepts.
The paper tackles the problem of enabling natural and efficient communication between humans and robots in industrial collaboration, proposing a framework that uses multimodal fusion of voice and gesture commands while ensuring safety compliance, with validation showing the robot can extract task-relevant information and adjust speed for safety.
The new industrial settings are characterized by the presence of human and robots that work in close proximity, cooperating in performing the required job. Such a collaboration, however, requires to pay attention to many aspects. Firstly, it is crucial to enable a communication between this two actors that is natural and efficient. Secondly, the robot behavior must always be compliant with the safety regulations, ensuring always a safe collaboration. In this paper, we propose a framework that enables multi-channel communication between humans and robots by leveraging multimodal fusion of voice and gesture commands while always respecting safety regulations. The framework is validated through a comparative experiment, demonstrating that, thanks to multimodal communication, the robot can extract valuable information for performing the required task and additionally, with the safety layer, the robot can scale its speed to ensure the operator's safety.