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WINVR 2010-3755 INTERFACES FOR 3 D FLIGHT PATH VISUALIZATION

Increased use of unmanned aerial vehicles on the battlefield is driving a transition of human operators into supervisory roles. In these roles, operators will have access to mission data and they will be required to make rapid decisions based on criteria, prior experience, and instincts. To facilitate rapid decisions, an interface must provide information in a format that operators can readily understand. A study was performed to investigate an operator’s ability to rapidly understand flight path data presented in either top-down 2D or perspective 3D. Additionally, the study aimed to explore the benefits of interactivity when observing the 3D scenarios. It was found that participants in the 3D group with automatic camera movement were not more accurate but were faster than participants who saw a top-down 2D view or a 3D view with manual camera control. This suggests that there may be benefits to a 3D interface for displaying three-dimensional path data. It also confirms that providing an interactive interface will not necessarily lead to higher performance, as the user may not use it efficiently. INTRODUCTION Military operations increasingly incorporate unmanned systems. In Operation Enduring Freedom and Operation Iraqi Freedom, almost 400,000 flight hours have been logged by Unmanned Aerial Vehicles (UAVs) [1]. According to the Department of Defense’s Unmanned Systems Roadmap 20072032 [2], these vehicles will be expected to perform a full range of mission tasks by 2030. UAVs will need to become increasingly autonomous to meet these performance requirements. That will not eliminate the need for human involvement, but will necessitate more efficient interaction as the vehicle to human ratio increases. Operators will transition into supervisory roles, monitoring and guiding the UAV activities through computer interfaces. One important area of supervisory control is the ability to re-task a UAV when unexpected circumstances arise. For example, a threat may be discovered along a vehicle’s current path. In such a situation, an operator may wish to perform reconnaissance on the threat, attack the threat, or avoid the threat altogether. There are also more global considerations, such as fuel use, that often must be factored into these decisions. Path planning is a complicated task involving visualization, optimization, prediction, and decision-making. When a scenario is already in progress, quality decisions often must be made within tight time-constraints. This study seeks to investigate the ability of novice operators to rapidly interpret three-dimensional (3D) path data using three different interfaces and re-task a UAV, given four alternate path choices. LITERATURE REVIEW A UAV operator interprets available information, using criteria, past experience, and intuition to choose the best course of action. The available information might include a visualization of the scenario along with other mission-specific metrics provided by a control interface. Criteria used when making a particular decision depend on mission objectives. Complete autonomy is undesirable for a UAV-control system because it would fail to properly incorporate intuition and past experience of a human operator. Additionally, serious accountability concerns could arise if certain decisions are made without the aid (or at least confirmation) of a human. Path Planning Algorithms Path planning algorithms attempt to provide one or more optimized paths to the operator. These algorithms use criteria to specify the minimum characteristics of an acceptable path. For example, paths can be generated to avoid collisions with the ground to prevent the UAV from crashing, so any output presented to the user will avoid the terrain [3]. Additionally some criteria can be weighted, allowing an algorithm to minimize fuel use while maximizing safety, for example. These algorithms perform well given the very specific weighted objectives that they are given. They can be very valuable to an