Reinforcement learning (RL) demonstrates superior potential over traditional flight control methods for fixed-wing aircraft, particularly under extreme operational conditions. However, the high demand for training samples and the lack of efficient computation in existing simulators hinder its further application. In this paper, we introduce NeuralPlane, the first benchmark platform for large-scale parallel simulations of fixed-wing aircraft. NeuralPlane significantly boosts high-fidelity simulation via GPU-accelerated Flight Dynamics Model (FDM) computation, achieving a single-step simulation time of just 0.2 seconds at a parallel scale of $10^{6}$, far exceeding current platforms. We also provide clear code templates, comprehensive evaluation/visualization tools and hierarchical frameworks for integrating RL and traditional control methods. We believe that NeuralPlane can accelerate the development of RL-based fixed-wing flight control and serve as a new challenging benchmark for the RL community.

The frame's origin is fixed at The motion equations are derived from Newton's second law for an air vehicle, resulting in six core The inputs for the FPEs are the aircraft's attitude quaternion components along with the components The system comprising (CLMEs)-(CAMEs)-(FPEs)-(KEs), i.e., 1, 12, 15, and 16, represents The task scenarios can be categorized by objectives into Heading, Control, and Tracking . This work designs a hierarchical control algorithm for this task. RL Methods We use PPO for Heading and Control tasks in fixed-wing aircraft. The structure for hierarchical RL method is shown in Figure 10. The PPO algorithm's parameter settings are as follows: the learning rate is set to "128 128", and the recurrent hidden layer size is 128 with a single recurrent layer.

Verifying the safety of controllers is critical for many applications, but is especially challenging for systems with bounded inputs. Backup control barrier functions (bCBFs) offer a structured approach to synthesizing safe controllers that are guaranteed to satisfy input bounds by leveraging the knowledge of a backup controller. While powerful, bCBFs require solving a high-dimensional quadratic program at run-time, which may be too costly for computationally-constrained systems such as aerospace vehicles. We propose an approach that optimally interpolates between a nominal controller and the backup controller, and we derive the solution to this optimization problem in closed form. We prove that this closed-form controller is guaranteed to be safe while obeying input bounds. We demonstrate the effectiveness of the approach on a double integrator and a nonlinear fixed-wing aircraft example.

-- Efficient real-time trajectory planning and control for fixed-wing unmanned aerial vehicles is challenging due to their non-holonomic nature, complex dynamics, and the additional uncertainties introduced by unknown aerodynamic effects. In this paper, we present a fast and efficient real-time trajectory planning and control approach for fixed-wing unmanned aerial vehicles, leveraging the differential flatness property of fixed-wing aircraft in coordinated flight conditions to generate dynamically feasible trajectories. The approach provides the ability to continuously replan trajectories, which we show is useful to dynamically account for the curvature constraint as the aircraft advances along its path. In recent years, the deployment of small Fixed-Wing Unmanned Aerial V ehicles (FW-UA Vs) has significantly increased across various applications, including environmental monitoring [1], low-altitude surveillance [2], and support for first responders in search and rescue operations [3]. Their popularity is primarily due to their superior endurance, extended operational range, and lower energy consumption compared to traditional V ertical Take-Off and Landing (VTOL) platforms like quadrotors. Since FW-UA Vs cannot hover in place or execute sharp turns and must maintain continuous motion to remain airborne, accurate trajectory planning and precise tracking are essential for their safe operations.

President Donald Trump has revealed the mysterious drones over New Jersey were'not the enemy' and had been authorized to conduct'research'. In the first press briefing of Trump's second administration, White House Press Secretary Karoline Leavitt said the Federal Aviation Administration (FAA) had been authorized to fly the drones for'research and various other reasons'. Leavitt said many of the drones were also'hobbyists, recreational and private individuals that enjoy flying drones' and claims that'in time, it got worse due to curiosity.' She added information had come'directly from the president of the United States that was just shared with me in the Oval Office'. But the White House's vague explanation has raised even more questions, especially after the FAA - which investigated the sightings after receiving reports from'concerned citizens' - failed to previously mention the alleged research.

A New Jersey Mayor has shared new footage of'glowing orbs transforming into drones' over Long Island, adding more intrigue to this ongoing mystery. Michael Melham, the Mayor of Belleville, has been outspoken about the unexplained phenomena plaguing his state and the greater tri-state area since mid-November when the drones first appeared. He shared the bizarre footage on X, saying the clips'appears to show glowing orbs turning into drones. Verified not to be planes via flight tracker. In a recent interview with NewsNation, Melham said he is still getting reports of drone sightings'all over New Jersey, and even Long Island.' 'Here in New Jersey, we are about 500 mayors strong, we are still waiting for answers because our residents are still gravely concerned over what's flying just over our homes,' he said.

Existing local dynamic route planning algorithms, when directly applied to terrain following/terrain avoidance, or dynamic obstacle avoidance for large and medium-sized fixed-wing aircraft, fail to simultaneously meet the requirements of real-time performance, long-distance planning, and the dynamic constraints of large and medium-sized aircraft. To deal with this issue, this paper proposes the Motion Dynamic RRT based Fluid Field - PPO for dynamic TF/TA routing planning. Firstly, the action and state spaces of the proximal policy gradient algorithm are redesigned using disturbance flow fields and artificial potential field algorithms, establishing an aircraft dynamics model, and designing a state transition process based on this model. Additionally, a reward function is designed to encourage strategies for obstacle avoidance, terrain following, terrain avoidance, and safe flight. Experimental results on real DEM data demonstrate that our algorithm can complete long-distance flight tasks through collision-free trajectory planning that complies with dynamic constraints, without the need for prior global planning.