Improving Tele-robotic Landmine Detection through Augmented Reality Devices

Abstract

Tele-robotic landmine/UXO (unexploded ordinance) detection and disposal is a real world example of a complex human robot interaction task. This study demonstrates that the use of augmented reality interfaces, designed based on a theoretically driven framework, can significantly improve performance, and that these same devices can be used as effective training technologies. Participants in this study were assigned to one of four experimental conditions (two uni-sensory and one multisensory augmented reality interface, and a control group). All participants were trained in tele-robotic landmine detection and the groups performed these tasks first with augmentation (session one) and then without (session two/transfer task). The results show that participants in the augmented groups significantly outperformed the control group in terms of the numbers of landmines correctly identified (in both sessions). The multisensory condition was found to result in both significantly increased landmine detection and significantly decreased task time (in both sessions).

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