In modern air traffic management, generating synthetic flight trajectories has emerged as a promising solution for addressing data scarcity, protecting sensitive information, and supporting large-scale analyses. In this paper, we propose a novel method for trajectory synthesis by adapting the Time-Based Vector Quantized Variational Autoencoder (TimeVQVAE). Our approach leverages time-frequency domain processing, vector quantization, and transformer-based priors to capture both global and local dynamics in flight data. By discretizing the latent space and integrating transformer priors, the model learns long-range spatiotemporal dependencies and preserves coherence across entire flight paths. We evaluate the adapted TimeVQVAE using an extensive suite of quality, statistical, and distributional metrics, as well as a flyability assessment conducted in an open-source air traffic simulator. Results indicate that TimeVQVAE outperforms a temporal convolutional VAE baseline, generating synthetic trajectories that mirror real flight data in terms of spatial accuracy, temporal consistency, and statistical properties. Furthermore, the simulator-based assessment shows that most generated trajectories maintain operational feasibility, although occasional outliers underscore the potential need for additional domain-specific constraints. Overall, our findings underscore the importance of multi-scale representation learning for capturing complex flight behaviors and demonstrate the promise of TimeVQVAE in producing representative synthetic trajectories for downstream tasks such as model training, airspace design, and air traffic forecasting.

We present flight patterns for a collision-free passage of swarms of drones through one or more openings. The narrow openings provide drones with access to an infrastructure component such as charging stations to charge their depleted batteries and hangars for storage. The flight patterns are a staging area (queues) that match the rate at which an infrastructure component and its openings process drones. They prevent collisions and may implement different policies that control the order in which drones pass through an opening. We illustrate the flight patterns with a 3D display that uses drones configured with light sources to illuminate shapes.

Drones are everywhere these days, filming dramatic reveals and awe-inspiring scenery for social media platforms. The problem is, they're not exactly approachable for beginners who have only ever used a smartphone. Last month, Snap debuted the $230 Pixy drone exactly for those people. It requires very little skill and acts like a personal robot photographer to help you produce nifty aerial shots. You don't need to pilot the Pixy.

The flight patterns of moths could help scientists to develop decision-making programs for autonomous drones to help them navigate unfamiliar environments. Researchers led from Washington State in the US analysed how moths flew through a simulated forest of light beams to create a drone navigation model to test. They found that the moths' navigation strategy is highly flexible and best suited for dense forests -- an adaptation that likely evolved in response to their habitat. By using real data from animal flight paths, the researchers said that they should be able to program drones to autonomously navigate cluttered environments. Biologist Thomas Daniel of the University of Washington in Seattle and colleagues mounted eight tobacco hawk moths -- or Mantuca sexta -- on the end of metal rods that were connected to a torque meter.

The albatross is one of the most efficient travelers in the animal world, and one species - the wandering albatross - can fly almost 500 miles (804 kilometers) in a day with just an occasional flap of its wings. The birds have a large wingspan of up to 11 feet (3.4 meters) across to catch and ride the wind, and soar and dive between contrasting currents of air, a flight pattern called dynamic soaring - comparable to riding a sidewinding rollercoaster. Now, engineers at MIT have developed a new model to simulate this dynamic soaring, and have used it to identify the optimal flight pattern that an albatross should take in order to harvest the most wind and energy. They found that when an albatross banks or turns to dive down and soar up, it should do this in shallow arcs - keeping almost to a straight, forwards track. The researchers say that the new model will be useful in understanding how albatross flight patterns may change as wind patterns shift with changing climate, as well as inform the design of wind-propelled drones and gliders which could be used to perform long-duration, long-range monitoring missions in remote regions of the world.