An AGI-Inspired Cognitive Safety System for Real-Time Logistics and Warehouse Accident Prevention

Abstract

Industrial logistics and warehouse environments are still plagued by accidents owing to the reactive characteristics of the safety systems in place, which are primarily rule-based or frame-based. The present paper discusses an architecture for a cognitive safety system inspired by AGI for accident prevention in industrial environments in real-time. The architecture is based on a combination of structured perception, prediction of the future state of the world, symbolic reasoning, experience-based memory, and safety-constrained decision planning in a closed loop. The architecture also simulates the future state of the world before executing an action, considers alternative courses of action, and improves its behavior through self-evaluation following a decision. The architecture also includes modules for modeling human compliance with safety warnings, learning the operational costs of safety interventions, and zero-shot learning across different environments. The architecture also includes the injection of deliberate errors and the monitoring of the stability of the decisions. The architecture is designed to operate in real-time and is tested in representative scenarios in a warehouse environment. The results show the effectiveness of the cognitive safety architecture in the prevention of accidents in industrial environments.