Enrico Ferrentino

Vincenzo Petrone, Enrico Ferrentino, Pasquale Chiacchio

This position paper explores the integration of Artificial Intelligence (AI) into force-controlled robotic tasks within the scope of advanced manufacturing, a cornerstone of Industry 4.0. AI's role in enhancing robotic manipulators - key drivers in the Fourth Industrial Revolution - is rapidly leading to significant innovations in smart manufacturing. The objective of this article is to frame these innovations in practical force-controlled applications - e.g. deburring, polishing, and assembly tasks like peg-in-hole (PiH) - highlighting their necessity for maintaining high-quality production standards. By reporting on recent AI-based methodologies, this article contrasts them and identifies current challenges to be addressed in future research. The analysis concludes with a perspective on future research directions, emphasizing the need for common performance metrics to validate AI techniques, integration of various enhancements for performance optimization, and the importance of validating them in relevant scenarios. These future directions aim to provide consistency with already adopted approaches, so as to be compatible with manufacturing standards, increasing the relevance of AI-driven methods in both academic and industrial contexts.

Enrico Ferrentino, Pasquale Chiacchio, Giovanni Russo

We present the principled design of a control pipeline for the synthesis of policies from examples data. The pipeline, based on a discretized design which we term as discrete fully probabilistic design, expounds an algorithm recently introduced in Gagliardi and Russo (2021) to synthesize policies from examples for constrained, stochastic and nonlinear systems. Contrary to other approaches, the pipeline we present: (i) does not need the constraints to be fulfilled in the possibly noisy example data; (ii) enables control synthesis even when the data are collected from an example system that is different from the one under control. The design is benchmarked numerically on an example that involves controlling an inverted pendulum with actuation constraints starting from data collected from a physically different pendulum that does not satisfy the system-specific actuation constraints. We also make our fully documented code openly available.

Federica Storiale, Enrico Ferrentino, Pasquale Chiacchio

Aerospace production volumes have increased over time and robotic solutions have been progressively introduced in the aeronautic assembly lines to achieve high-quality standards, high production rates, flexibility and cost reduction. Robotic workcells are sometimes characterized by robots mounted on slides to increase the robot workspace. The slide introduces an additional degree of freedom, making the system kinematically redundant, but this feature is rarely used to enhance performances. The paper proposes a new concept in trajectory planning, that exploits the redundancy to satisfy additional requirements. A dynamic programming technique is adopted, which computes optimized trajectories, minimizing or maximizing the performance indices of interest. The use case is defined on the LABOR (Lean robotized AssemBly and cOntrol of composite aeRostructures) project which adopts two cooperating six-axis robots mounted on linear axes to perform assembly operations on fuselage panels. Considering the needs of this workcell, unnecessary robot movements are minimized to increase safety, the mechanical stiffness is maximized to increase stability during the drilling operations, collisions are avoided, while joint limits and the available planning time are respected. Experiments are performed in a simulation environment, where the optimal trajectories are executed, highlighting the resulting performances and improvements with respect to non-optimized solutions.