A Unification Between Deep-Learning Vision, Compartmental Dynamical Thermodynamics, and Robotic Manipulation for a Circular Economy

The shift from a linear to a circular economy has the potential to simultaneously reduce uncertainties of material supplies and waste generation. However, to date, the development of robotic and, more generally, autonomous systems have been rarely integrated into circular economy implementation strategies despite their potential to reduce the operational costs and the contamination risks from handling waste. In addition, the science of circularity still lacks the physical foundations needed to improve the accuracy and the repeatability of the models. Hence, in this paper, we merge deep-learning vision, compartmental dynamical thermodynamics, and robotic manipulation into a theoretically-coherent physics-based research framework to lay the foundations of circular flow designs of materials. The proposed framework tackles circularity by generalizing the design approach of the Rankine cycle enhanced with dynamical systems theory. This differs from state-of-the-art approaches to circular economy, which are mainly based on data analysis, e.g., material flow analysis (MFA). We begin by reviewing the literature of the three abovementioned research areas, then we introduce the proposed unified framework and we report the initial application of the framework to plastics systems along with initial simulation results of reinforcement-learning control of robotic waste sorting. This shows the framework applicability, generality, scalability, and the similarity and difference between the optimization of artificial neural systems and the proposed compartmental networks. Finally, we discuss the still not fully exploited opportunities for robotics in circular economy and the future challenges in the theory and practice of the proposed circularity framework.