Safe and Transparent Robots for Human-in-the-Loop Meat Processing

2025-08-20

AI Summary

The meat processing industry faces severe labor shortages, while existing automation systems suffer from low flexibility, high cost, and poor adaptability. To address these challenges, this paper proposes a general-purpose human–robot collaborative robotic system specifically designed for meat processing, focusing on two core challenges: safety and transparency. First, we introduce a dynamic safety framework integrating real-time hand detection and force-sensing cutting tools to enable contact object identification and adaptive emergency stop. Second, we design a multimodal intent communication mechanism—comprising LED-based confidence indicators and a graphical interactive interface—that visualizes the robot’s real-time decision-making and cutting plans while supporting operator feedback. Evaluated on an industrial production line, the system significantly improves operators’ comprehension of and trust in robotic behavior, enhances collaborative safety and fluency, and establishes a scalable, human-centered paradigm for flexible meat processing.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Natural Language Processing: Safety and Robustness
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Data transparency and provenance

Abstract

Labor shortages have severely affected the meat processing sector. Automated technology has the potential to support the meat industry, assist workers, and enhance job quality. However, existing automation in meat processing is highly specialized, inflexible, and cost intensive. Instead of forcing manufacturers to buy a separate device for each step of the process, our objective is to develop general-purpose robotic systems that work alongside humans to perform multiple meat processing tasks. Through a recently conducted survey of industry experts, we identified two main challenges associated with integrating these collaborative robots alongside human workers. First, there must be measures to ensure the safety of human coworkers; second, the coworkers need to understand what the robot is doing. This paper addresses both challenges by introducing a safety and transparency framework for general-purpose meat processing robots. For safety, we implement a hand-detection system that continuously monitors nearby humans. This system can halt the robot in situations where the human comes into close proximity of the operating robot. We also develop an instrumented knife equipped with a force sensor that can differentiate contact between objects such as meat, bone, or fixtures. For transparency, we introduce a method that detects the robot's uncertainty about its performance and uses an LED interface to communicate that uncertainty to the human. Additionally, we design a graphical interface that displays the robot's plans and allows the human to provide feedback on the planned cut. Overall, our framework can ensure safe operation while keeping human workers in-the-loop about the robot's actions which we validate through a user study.

Problem

• Addressing labor shortages with collaborative robots in meat processing
• Ensuring human safety through proximity detection and contact differentiation
• Enhancing transparency via uncertainty communication and interactive planning interfaces

Innovation

• Hand-detection system for human safety monitoring
• Instrumented knife with force sensor differentiation
• LED interface communicating robot uncertainty to humans