On the role of Artificial Intelligence methods in modern force-controlled manufacturing robotic tasks

2024-09-25

International Conference on Informatics in Control, Automation and Robotics

AI Summary

Force-sensitive tasks in advanced manufacturing—such as deburring, polishing, and pin-hole assembly—demand precise, robust, and generalizable force control, yet current AI-driven approaches (e.g., deep learning, reinforcement learning, adaptive control, and multimodal sensing) face critical bottlenecks in accuracy, robustness, and generalization, exacerbated by fragmented evaluation protocols, insufficient cross-technique integration, and limited real-world production-line validation. Method: This work systematically reviews AI-enabled force control methodologies and proposes three innovations: (1) a unified benchmark for quantitative force-control performance evaluation; (2) a multi-technique synergistic optimization framework integrating perception, learning, and control; and (3) end-to-end empirical validation in Industry 4.0–aligned industrial scenarios. Contribution/Results: The study delivers a reproducible research roadmap for academia and an ISO/IEC-compliant AI force-control deployment framework for industry, significantly enhancing the practicality and deployability of AI in high-precision, flexible assembly.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Interaction Techniques and Devices

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Artificial Intelligence
• Robot Force Control
• Manufacturing Applications

Innovation

• Artificial Intelligence
• Advanced Manufacturing
• Robot Control Optimization