Visual augmentation of live-streaming images in virtual reality to enhance teleoperation of unmanned ground vehicles

First-person view (FPV) technology in virtual reality (VR) can offer in-situ environments in which teleoperators can manipulate unmanned ground vehicles (UGVs). However, non-experts and expert robot teleoperators still have trouble controlling robots remotely in various situations. For example, obstacles are not easy to avoid when teleoperating UGVs in dim, dangerous, and difficult-to-access areas with environmental obstacles, while unstable lighting can cause teleoperators to feel stressed. To support teleoperators’ ability to operate UGVs efficiently, we adopted construction yellow and black lines from our everyday life as a standard design space and customised the Sobel algorithm to develop VR-mediated teleoperations to enhance teleoperators’ performance. Our results show that our approach can improve user performance on avoidance tasks involving static and dynamic obstacles and reduce workload demands and simulator sickness. Our results also demonstrate that with other adjustment combinations (e.g., removing the original image from edge-enhanced images with a blue filter and yellow edges), we can reduce the effect of high-exposure performance in a dark environment on operation accuracy. Our present work can serve as a solid case for using VR to mediate and enhance teleoperation operations with a wider range of applications.