Design of Advanced Human–Robot Collaborative Cells for Personalized Human–Robot Collaborations

Abstract

Industry 4.0 is pushing forward the need for symbiotic interactions between physical and virtual entities of production environments to realize increasingly flexible and customizable production processes. This holds especially for human–robot collaboration in manufacturing, which needs continuous interaction between humans and robots. The coexistence of human and autonomous robotic agents raises several methodological and technological challenges for the design of effective, safe, and reliable control paradigms. This work proposes the integration of novel technologies from Artificial Intelligence, Control and Augmented Reality to enhance the flexibility and adaptability of collaborative systems. We present the basis to advance the classical human-aware control paradigm in favor of a user-aware control paradigm and thus personalize and adapt the synthesis and execution of collaborative processes following a user-centric approach. We leverage a manufacturing case study to show a possible deployment of the proposed framework in a real-world industrial scenario.

Hiding task-oriented programming complexity: an industrial case study

“…The goal of Robo-Partner was to bring robotics closer to SMEs where poor robotic knowledge represents a barrier to robot adoption. Sharework aims to introduce dynamic task planning able to assign the robot actions to the robot controllers autonomously (Umbrico et al, 2022). The assignment evaluates the evolution of a collaborative task controlling the robot through ROS libraries (Faroni et al, 2020).…”

Enhanced Cognition for Adaptive Human-Robot Collaboration

“…Artificial Intelligence (AI) can play a role in endowing controllers and thus robots with the cognitive skills needed to effectively and reliably behave in such scenarios [10], [11]. Towards this goal, we are investigating the use of novel AI technologies in HRC to allow collaborative robots to: (i) perceive the environment [12], correctly interpret occurring events and situations to build and maintain knowledge about production contexts [13]; (ii) reason about their own capabilities/skills and dynamically contextualize possible actions according to the known state of a production scenario [14], [15] and; (iii) autonomously decide how to interact with the environment and other "actors" (i.e., human operators but even other robots if necessary) in order to carry out tasks and dynamically support production needs [16]- [18].…”