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Mobile robots are increasingly becoming integral to human-centered environments,
ranging from industrial automation to assistive applications. As their role expands,
ensuring effective and intuitive interaction with robots is essential, particularly for tasks in
volving navigation in dynamic environments. This requires the development of advanced
control mechanisms capable of controlling robots with high precision while simultaneously
avoiding obstacles. Traditionally, teleoperation via remote control devices such as
joysticks has served as the standard interface for navigation. However, these systems
demand continuous mental focus, offer limited situational awareness due to indirect visual
feedback, which contributes to a higher cognitive load. To address these challenges,
Augmented Reality (AR) provides spatially immersive interfaces that enhance situational
awareness by providing direct, intuitive visual understanding of the environment. In this
research, we evaluate the cognitive load of AR integrated with Motion planning algorithm
using the NASA Task Load Index (TLX) score. Experiments were conducted with
participants from diverse technical and demographic backgrounds, comparing AR-based
navigation with traditional teleoperation. The results demonstrated a 30.09% reduction in
cognitive load when using AR compared to remote control. These findings suggest that
AR-based systems improve ease of use and accessibility, making them valuable for
assistive robotic applications.