Synergy Over Spiral: A Logistics 5.0 Game-Theoretic Model for Trust-Fatigue Co-regulation in Human-Cobot Order Picking

TL;DR

This paper models trust and fatigue in human-cobot order picking using a game-theoretic approach, demonstrating how refined trust management can significantly boost productivity and system resilience in Logistics 5.0.

Contribution

It introduces a novel Stackelberg game model incorporating trust and fatigue, and shows how trust-synergy cycles improve productivity and robustness in human-robot collaboration.

Findings

Trust-synergy cycle increases productivity by nearly 100%.

Trust-Recovery Mode reduces trust recovery time by over 75%.

Naive trust models lead to trust death spirals, harming system performance.

Abstract

This paper investigates the critical role of trust and fatigue in human-cobot collaborative order picking, framing the challenge within the scope of Logistics 5.0: the implementation of human-robot symbiosis in smart logistics. We propose a dynamic, leader-follower Stackelberg game to model this interaction, where utility functions explicitly account for human fatigue and trust. Through agent-based simulations, we demonstrate that while a naive model leads to a "trust death spiral," a refined trust model creates a "trust synergy cycle," increasing productivity by nearly 100 percent. Finally, we show that a cobot operating in a Trust-Recovery Mode can overcome system brittleness after a disruption, reducing trust recovery time by over 75 percent compared to a non-adaptive model. Our findings provide a framework for designing intelligent cobot behaviors that fulfill the Industry 5.0 pillars of human-centricity, sustainability, and resilience.