Augmented Reality (AR) applications increasingly permeate domains such as gaming, retail, education, and healthcare. Despite their rapid adoption, systematic testing of AR apps remains underexplored due to their dependence on complex real-world contexts, sensor data, and diverse user interactions. In this paper, we propose ARTRIP (Automatic AR Testing with Randomized Interaction Patterns), a novel testing technique designed to explore AR applications with randomized interaction patterns. Unlike existing random testing approaches such as Monkey, ARTRIP uses a randomized interaction pattern to enhance the chance of covering more complicated interaction sequences. We describe the methodology, present a prototype implementation, and evaluate its effectiveness through case studies on four popular AR apps. Results suggest that ARTRIP achieves higher coverage than Monkey, highlighting its potential as a practical AR testing framework.