ARTOO-DARTU: Studying AR-HRC With AR Obstruction Mitigation During a Warehouse Task

2026-06-23

AI Summary

This study addresses safety and usability challenges in augmented reality (AR)-mediated human-robot collaboration (HRC) for warehouse tasks, where AR overlays often occlude critical real-world elements. To mitigate this issue, the authors propose ARTOO-DARTU, a novel system incorporating the first obstacle detection and mitigation (ODM) mechanism specifically designed for AR-HRC. By dynamically repositioning AR content based on real-time scene analysis, ODM preserves clear visibility of the physical environment while delivering essential robot contextual information. The approach integrates context-aware visualization with real-time occlusion avoidance and is evaluated through a gamified experimental platform, Pocket MonstARs. User studies demonstrate that enabling ODM improves overall task efficiency by 46% and accelerates subtasks reliant on unobstructed real-world vision by 61%, substantially enhancing the AR-HRC user experience.

Abstract

Human-robot collaboration (HRC) often requires robot intentions and internal states to be conveyed to users for task efficiency and safety. Recently, augmented reality (AR) situated analytics provide such real-time robot feedback in HRC contexts. However, AR situated analytics can obstruct important real-world elements, posing safety and usability risks, especially when content is dynamically positioned relative to movements of mobile robots in a warehouse HRC scenario. In this paper, we introduce the Augmented Reality Technique Of Obstruction Deterrence while Aiding Robotic Teaming for Users (ARTOO-DARTU), an AR system tailored specifically for warehouse HRC that enables real-time robot situated analytics and control while preserving visibility of the real world through an obstruction detection and mitigation pipeline (ODM) that is uniquely suited for AR-HRC. To evaluate ARTOO-DARTU, we developed Pocket MonstARs, a controlled gamified abstraction of HRC warehouse inventory picking in which virtual monsters serve as proxies for pick targets, while labeled and object-marked boxes preserve the real-world identification demands of the picking task. In a 34-participant user study, we found that our designed AR situated analytics yielded a 46% increase in efficiency on the overall HRC task, but only when the ODM was active. Participants with the ODM active were also 61% faster on subtasks requiring visibility of the real world. Our findings demonstrate that, when paired with our developed ODM to prevent real-world obstructions, the situated analytics in ARTOO-DARTU can significantly enhance efficiency and user experience in AR-HRC warehouse scenarios.

Problem

• augmented reality
• human-robot collaboration
• obstruction mitigation
• warehouse task
• situated analytics

Innovation

• Augmented Reality
• Human-Robot Collaboration
• Obstruction Mitigation
• Situated Analytics
• Warehouse Automation