Cooperative Planning System for Self-Separation in En-route Airspace

The increase in flight density and the need to integrate Unmanned Areal Vehicles into the National Airspace demands higher flexibility. Distributing the conflict detection and resolution (CD&R) functions among the aircraft ensures a greater flexibility in the flight plans for the aircraft. A co-operative planning system is proposed for separation assurance by distributing the CD&R in the en-route airspace among a fully-connected network of aircraft. The aircraft cooperate to achieve the common goal of conflict-free trajectories, while attempting to reduce the disruptions from their original flight plans. A pairwise CD&R algorithm is developed through heuristics which is then implemented iteratively to obtain the solution. Coordination of the aircraft maneuvers in the distributed CD&R algorithm is ensured implicitly through geometric criteria and explicitly through communication for multiple conflicts. Furthermore, a novel robust aircraft trajectory model using cubic Bezier parametric curves is developed, which gives an accurate, minimalistic representation of flight paths for the algorithm to act on and modify for new resolutions. The algorithm is validated by sweeping through different parameters for a two aircraft configuration and also compared with a benchmark tactical CD&R algorithm. Furthermore, the planning system is shown to be feasible for implementation with the current ADS-B surveillance technology

Deep Learning Framework for Trajectory Prediction and In-time Prognostics in the Terminal Airspace

Terminal airspace around an airport is the biggest bottleneck for commercial operations in the National Airspace System (NAS). In order to prognosticate the safety status of the terminal airspace, effective prediction of the airspace evolution is necessary. While there are fixed procedural structures for managing operations at an airport, the confluence of a large number of aircraft and the complex interactions between the pilots and air traffic controllers make it challenging to predict its evolution. Modeling the high-dimensional spatio-temporal interactions in the airspace given different environmental and infrastructural constraints is necessary for effective predictions of future aircraft trajectories that characterize the airspace state at any given moment. A novel deep learning architecture using Graph Neural Networks is proposed to predict trajectories of aircraft 10 minutes into the future and estimate prog?nostic metrics for the airspace. The uncertainty in the future is quantified by predicting distributions of future trajectories instead of point estimates. The framework’s viability for trajectory prediction and prognosis is demonstrated with terminal airspace data from Dallas Fort Worth International Airport (DFW). </p