As the capability and complexity of UAVs continue to increase, the human-robot interface community has a responsibility to design better ways of specifying the complex 3D flight paths necessary for instructing them. Immersive interfaces, such as those afforded by virtual reality (VR), have several unique traits which may improve the user's ability to perceive and specify 3D information. These traits include stereoscopic depth cues which induce a sense of physical space as well as six degrees of freedom (DoF) natural head-pose and gesture interactions. This work introduces an open-source platform for 3D aerial path planning in VR and compares it to existing UAV piloting interfaces. Our study has found statistically significant improvements in safety and subjective usability over a manual control interface, while achieving a statistically significant efficiency improvement over a 2D touchscreen interface. The results illustrate that immersive interfaces provide a viable alternative to touchscreen interfaces for UAV path planning.

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In hazardous environments where direct human operation of machinery is not possible, such as in a nuclear power plant (NPP), teleoperation may be utilized to complete tasks safely. However, because teleoperation tasks are time consuming, complex, highly difficult, and need to be performed with incomplete information, they may increase the human operator’s cognitive load, which can affect the efficiency of the human operator as well as the completeness of the task. In this study, we propose a teleoperation system using a hybrid teleoperation controller that can increase operator efficiency for specific teleoperation tasks in complex sequences. First, we decomposed the task into a sequence of unit subtasks. For each subtask, the input space was allocated, and either position control or rate control by the hybrid controller was determined. Teleoperation experiments were conducted to verify the controller. To evaluate the efficiency improvement of the teleoperators, the completion time and NASA task-load index (NASA-TLX) were measured. Using the hybrid controller reduced the completion time and the NASA-TLX score by 17.23% and 34.02%, respectively, compared to the conventional position controller.