Enhanced Cognition for Adaptive Human-Robot Collaboration

Abstract

Cyber-Physical Systems constitute one of the core concepts in Industry 4.0 aiming at realizing production systems that combine the efforts of human workers, robots, and intelligent entities. This is particularly crucial in Human-Robot Collaboration manufacturing where a tight peer-to-peer interaction between humans and intelligent autonomous robots is necessary. The work proposes the integration of novel Artificial Intelligence technologies to enhance the flexibility and adaptability of collaborative robots. The integrated functionalities allow a collaborative robot to autonomously recognize the tasks a human worker performs, and accordingly adapt its behavior. The approach is deployed on a real HRC scenario showing the functioning of the developed cognitive capabilities and the increased flexibility of resulting collaborations.

Enhancing awareness of industrial robots in collaborative manufacturing

“…The described reactive behavior represents the actual way the developed knowledge representation and reasoning modules have been deployed into this pilot case [88]. To evaluate the representation capabilities of the ontological model and the task planning model, we here model the whole production process assuming a deliberative approach similar to other use cases.…”

Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts

“…For instance, [37] handles the coordination of heterogeneous agents using a MILP formalization and constrained scheduling for aircraft assembly. [38] and [39] apply timeline-based planning in TAMP framework to a semi-automated assembly/disassembly industrial case study. [40] proposed a task allocation technique for HRC using a neural network for electric vehicle battery disassembly.…”