This paper derives various Hessians associated with Birkhoff-theoretic methods for trajectory optimization. According to a theorem proved in this paper, approximately 80% of the eigenvalues are contained in the narrow interval [-2, 4] for all Birkhoff-discretized optimal control problems. A preliminary analysis of computational complexity is also presented with further discussions on the grand challenge of solving a million point trajectory optimization problem.

Stop-rotor aircraft have long been proposed as the ideal vertical takeoff and landing (VTOL) aircraft for missions with equal time spent in both flight regimes, such as agricultural monitoring, search and rescue, and last-mile delivery. Featuring a central lifting surface that rotates in VTOL to generate vertical thrust and locks in forward flight to generate passive lift, the stop-rotor offers the potential for high efficiency across both modes. However, practical implementation has remained infeasible due to aerodynamic and stability conflicts between flight modes. In this work, we present SPERO (Stopped-Penta Rotor), a stop-rotor uncrewed aerial vehicle (UAV) featuring a flipping and latching wing, an active center of pressure mechanism, thrust vectored counterbalances, a five-rotor architecture, and an eleven-state machine flight controller coordinating geometric and controller reconfiguration. Furthermore, SPERO establishes a generalizable design and control framework for stopped-rotor UAVs. Together, these innovations overcome longstanding challenges in stop-rotor flight and enable the first stable, bidirectional transition between VTOL and forward flight.

The volume of textual data available in aviation safety reports presents a challenge for timely and accurate analysis. This paper examines how Artificial Intelligence (AI) and, specifically, Natural Language Processing (NLP) can automate the process of extracting valuable insights from this data, ultimately enhancing aviation safety. The paper reviews ongoing efforts focused on the application of NLP and deep learning to aviation safety reports, with the goal of classifying the level of damage to an aircraft and identifying the phase of flight during which safety occurrences happen. Additionally, the paper explores the use of Topic Modeling (TM) to uncover latent thematic structures within aviation incident reports, aiming to identify recurring patterns and potential areas for safety improvement. The paper compares and contrasts the performance of various deep learning models and TM techniques applied to datasets from the National Transportation Safety Board (NTSB) and the Australian Transport Safety Bureau (ATSB), as well as the Aviation Safety Network (ASN), discussing the impact of dataset size and source on the accuracy of the analysis. The findings demonstrate that both NLP and deep learning, as well as TM, can significantly improve the efficiency and accuracy of aviation safety analysis, paving the way for more proactive safety management and risk mitigation strategies.

Models for trajectory prediction are an essential component of many advanced air mobility studies. These models help aircraft detect conflict and plan avoidance maneuvers, which is especially important in Unmanned Aircraft systems (UAS) landing management due to the congested airspace near vertiports. In this paper, we propose a landing trajectory prediction model for UAS based on Generative Adversarial Network (GAN). The GAN is a prestigious neural network that has been developed for many years. In previous research, GAN has achieved many state-of-the-art results in many generation tasks. The GAN consists of one neural network generator and a neural network discriminator. Because of the learning capacity of the neural networks, the generator is capable to understand the features of the sample trajectory. The generator takes the previous trajectory as input and outputs some random status of a flight. According to the results of the experiences, the proposed model can output more accurate predictions than the baseline method(GMR) in various datasets. To evaluate the proposed model, we also create a real UAV landing dataset that includes more than 2600 trajectories of drone control manually by real pilots.

Fluidic injection provides a promising solution to improve the performance of overexpanded single expansion ramp nozzle (SERN) during vehicle acceleration. However, determining the injection parameters for the best overall performance under multiple nozzle operating conditions is still a challenge. The gradient-based optimization method requires gradients of injection parameters at each design point, leading to high computational costs if traditional computational fluid dynamic (CFD) simulations are adopted. This paper uses a pretrained neural network model to replace CFD during optimization to quickly calculate the nozzle flow field at multiple design points. Considering the physical characteristics of the nozzle flow field, a prior-based prediction strategy is adopted to enhance the model's transferability. In addition, the back-propagation algorithm of the neural network is adopted to quickly evaluate the gradients by calling the computation process only once, thereby greatly reducing the gradient computation time compared to the finite differential method. As a test case, the average nozzle thrust coefficient of a SERN at seven design points is optimized. An improvement in the thrust coefficient of 1.14% is achieved, and the time cost is greatly reduced compared with the traditional optimization methods, even when the time to establish the database for training is considered.

Safety is a critical concern for urban flights of autonomous Unmanned Aerial Vehicles. In populated environments, risk should be accounted for to produce an effective and safe path, known as risk-aware path planning. Risk-aware path planning can be modeled as a Constrained Shortest Path (CSP) problem, aiming to identify the shortest possible route that adheres to specified safety thresholds. CSP is NP-hard and poses significant computational challenges. Although many traditional methods can solve it accurately, all of them are very slow. Our method introduces an additional safety dimension to the traditional A* (called ASD A*), enabling A* to handle CSP. Furthermore, we develop a custom learning-based heuristic using transformer-based neural networks, which significantly reduces the computational load and improves the performance of the ASD A* algorithm. The proposed method is well-validated with both random and realistic simulation scenarios.

Unmanned aerial vehicles (UAVs) are becoming an integral part of both industry and society. In particular, the quadrotor is now invaluable across a plethora of fields and recent developments, such as the inclusion of aerial manipulators, only extends their versatility. As UAVs become more widespread, preventing loss-of-control (LOC) is an ever growing concern. Unfortunately, LOC is not clearly defined for quadrotors, or indeed, many other autonomous systems. Moreover, any existing definitions are often incomplete and restrictive. A novel metric, based on actuator capabilities, is introduced to detect LOC in quadrotors. The potential of this metric for LOC detection is demonstrated through both simulated and real quadrotor flight data. It is able to detect LOC induced by actuator faults without explicit knowledge of the occurrence and nature of the failure. The proposed metric is also sensitive enough to detect LOC in more nuanced cases, where the quadrotor remains undamaged but nevertheless losses control through an aggressive yawing manoeuvre. As the metric depends only on system and actuator models, it is sufficiently general to be applied to other systems.

Kurt Knutsson talks about an innovative robot that can explore the depths of the ocean and capture stunning photos and videos. Drones are everywhere these days. They can fly, swim, and even transform into different shapes. They can deliver packages, be used to spy, pick fruits, and even explore the ocean depths. Some of them are downright creepy.