Design and Validation of Safety Envelopes in Human-Robot Collaborative Workspaces Using Real-Time Motion Prediction

Abstract

Industrial automation has increasingly shifted toward human-robot collaborative environments where traditional safety approaches prove insufficient for maintaining both productivity and worker safety. This paper presents a novel framework for constructing dynamic safety envelopes around robotic systems operating in shared workspaces with human operators. Our approach utilizes advanced spatiotemporal motion prediction models to anticipate human movements and dynamically adjusts safety boundaries based on predicted trajectories, task contexts, and operational parameters. We propose a computational model that integrates stochastic process analysis with non-Euclidean geometry to represent these adaptive safety fields. Experimental validation in a simulated manufacturing environment demonstrated a 37.5\% reduction in unnecessary safety-triggered halts while maintaining a 99.8\% collision prevention rate. The system showed particularly strong performance in high-variability tasks, where traditional fixed-boundary systems typically exhibit either excessive conservatism or inadequate protection. This framework enables more natural human-robot collaboration without compromising safety standards, potentially increasing collaborative workspace efficiency by 22\% to 28\% compared to conventional methods, while providing formal guarantees within specified confidence intervals.