On the Added Value of Mmwave Radar Within Indoor Logistic Environments
Speed and Separation Monitoring (SSM) is a key enabler for productivity in human-robot collaborative applications, yet its industrial use remains limited by the difficulty of reliably obtaining human-related motion parameters. Although recent research explores AI-based perception to address this challenge, the probabilistic nature and lack of certification of AI methods prevent their deployment as safety-critical systems. This work investigates deterministic strategies for improving SSM efficiency without relying on AI, focusing on zone-adaptation techniques and sensing capabilities that remain fully compatible with functional-safety standards. The work contributes (1) a structured analysis of how motion-adaptive zone formulations can reduce conservatism in current SSM implementations, and (2) a performance-oriented comparison of safety-certified LiDAR and radar sensors regarding velocity measurement. The analysis shows that while LiDAR continues to dominate industrial practice, radar offers several complementary advantages that may significantly enhance SSM performance. These findings motivate further exploration of deterministic, safety-compliant processing methods.