Flight Evaluation of In-Flight Strategic Path Planning Automation for Future High-Density Operations

Advanced Air Mobility (AAM) is a vision for a future air transportation system that serves new aviation markets, relying on a new generation of technologically advanced aircraft. One potential new market is air transportation within metropolitan areas. Urban Air Mobility (UAM) in the long term envisions thousands of simultaneous air operations over urban areas. It is a key element of the broader AAM vision, delivering economic benefits and alleviating ground congestion. UAM introduces significant challenges in the management of flight operations. The Federal Aviation Administration and NASA propose concepts for evolutionary development toward high-density urban operations. Initial low-tempo UAM operations will take place in the current regulatory and operational environment, followed by evolutionary changes to increase operational tempo. As a complement to these near-term activities, this study takes a transformational approach, addressing a key technical challenge in managing future high-density operations.
Human operators may not reside on UAM aircraft in the future. Even if they do, high traffic density and operational complexity may make traditional human operator tasks challenging. AAM operations are therefore anticipated to rely on collaborative and responsible automation. Flight Path Management (FPM) is an automation concept that may be a critical functional enabler in future operations. FPM provides the function of strategic and dynamic flight path planning in the presence of other users sharing the airspace, and in the presence of restrictions and planning constraints necessary in the management of traffic flow.
A flight test of FPM was conducted using a reference prototype FPM automation system developed by NASA. Test goals included a verification of the automation’s core functions, exploration of function behaviors in a flight environment, discovery of unknown unknowns, and validation of the operating environment simulation used to develop the concept and technology. The test used two aircraft and a simulated high-traffic urban environment. Testing was performed using helicopters as surrogates for future electric vertical take-off and landing (eVTOL) aircraft. One helicopter was equipped with the reference prototype FPM automation system. Another helicopter was used as a cooperating intruder, sharing its flight intent with the automation equipped aircraft. The two aircraft engaged in conflict encounters in a live-virtual-constructive operating environment. A model of future high-density urban airspace was developed and populated with up to 330 virtual traffic aircraft that also cooperated by sharing their flight intent.
Flight test results provide positive indications that a vehicle-centric implementation of FPM can be made operational in the future. FPM has the potential to address critical challenges in managing the high-density traffic operations envisioned for long-term UAM operations. All functional performance success criteria for maneuvers in an airspace environment containing some area restrictions were met or exceeded. The research prototype automation technology reliably supported the core functions of intent-based conflict detection, strategic conflict resolution, conflict prevention, and arrival time compliance. Pilot feedback suggests human operators can be in the FPM decision-making loop at high traffic density. Test maneuvers involving operations within flow corridors were less successful, primarily due to having tighter volume constraints within the airspace and fewer degrees of freedom available for conflict resolution maneuvering. A specific trajectory management approach and its enabling automation technology need to be developed to support high-density operations within corridors. An initial simulation validation was performed using flight test data. Results suggest that with appropriate control of host platform capabilities, such as computational power, the existing simulation can be used to identify behavioral trends at both a vehicle level and an airspace system level.