3.8 Utilizing UASs to Assist in Weather Hazard Detection for Urban Air Mobility and Unmanned Traffic Management

Monday, 13 January 2020: 3:45 PM
206A (Boston Convention and Exhibition Center)
Jamey Jacob, Oklahoma State Univ., Stillwater, OK; and R. Allamraju, T. Mitchell, and V. Natalie

Current air vehicle capability and reach has benefited from an evolution in technology that is creating an entirely new ability to project power through the use of unmanned systems while reducing the risk to human life. The commercial success of unmanned air vehicles (UAVs) over the past decades has led to an emerging interest in the use of small unmanned aircraft systems (sUAS) as a potential tool for use in many urban applications, including delivery of goods and packages, traffic monitoring, and emergency response. While sUAS have seen tremendous development over the last decade, there are still a number of navigation, flight control, and performance challenges, not the least of which is the ability to withstand even moderate gusts. This is particularly true in urban environments where the presence of buildings produce complex, unsteady flow patterns. This will be an issue for the integration of urban air mobility (UAM) solutions as well as unmanned traffic management (UTM) system development and implementation. Both of these have important needs requiring improvement in technology that enables the capability to land in demanding urban locations, such as on roofs of buildings or high-rise landings. This requires testing and evaluation that allow a range of unsteady velocities to simulate take-off and landing speeds under gusty conditions.

Local wind-field sensing onboard UAVs is often approached via combining relative sensing and inertial reference measurements. Large scale wind field sensing is complicated by the need to accurately place an array of UAVs in a dynamic wind environment, and to sense that wind environment in parallel. Individual UAV wind field sensing has previously been approached by incorporating a relative wind estimation and an external position reference. However, the challenges of urban wind field sensing include high spatial turbulence gradients, high turbulence magnitudes, and degraded position references, all of which complicate the traditional approach.

Swarming and mesh network topologies are attractive for integrating additional sensing platforms into this measurement challenge. In addition to a reconfigurable communication network, the array of UAVs must physically be able to adapt itself to provide sensing of local wind features. However, accurate placement of UAVs in urban wind fields is complicated by the relatively large sensitivities of trajectories to wind gusts. Since understanding these wind fields is still a topic of research, and this turbulence is a large component of the wind field being sensed, a detailed understanding of gust responses is necessary to provide accurate control. Inertial estimation approaches that incorporate observational measurements and errors to provide measurement of the local wind field will improve their estimation accuracy by incorporating mechanisms to modulate the sensitivity based on local wind magnitudes. This presentation discusses integration approaches and field tests for utilizing UAS to provide real-time data to enable UAM and UTM operations; improving the resolution and accuracy of comprehensive wind field estimation is critical to improve safety and operational efficiency.

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