Combining Real-time & Offline Decision Making for Urban Air Mobility Systems

The goal of this project is to support safe operations of unmanned aerial vehicles (UAVs) for urban mobility. UAVs operations present hazards to other airspace users and people and property on the ground, and monitoring, analysis, and control schemas need to be developed to ensure that the risks attributed to these hazards are managed at acceptable levels. We are working in the design of algorithms and corresponding computational architectures that are directed to minimize risk in flight (i.e., maximize safety under the given operating conditions), while also performing degradation monitoring to support system-level prognostics and track UAV performance during a mission. 

Path planning (and replanning) algorithms that perform risk analysis conducted on projected UAV flight paths enable their safe operations. However, most approaches ignore the fact that the vehicle’s operating conditions can change during flight, and this can make the flight unsafe. Online monitoring of the UAV enables system‐level prognostics that combine the effects of multiple degrading components to determine how system functionality and performance are affected over the flight trajectory. Taking into account the degraded state of the UAV and the changes caused by environmental parameters, we have developed a path planning approach, currently based on fast genetic algorithms, to compute the mission trajectory using an online risk analysis methodology. Our approach invokes the planning (replanning) algorithm to revise the mission trajectory to reduce risks of crashing the vehicle, and to maintain overall system safety under current and predicted environmental conditions.