The world's first 360-degree drone is fun all around, if you don't mind the steep price or wearing goggles to control it. As someone who has been reviewing camera drones for over a decade, it's rare for me to encounter one that feels genuinely new. While DJI's continual stream of steadily improving, ever-reliable drones almost always impresses, what Antigravity has done with its first-ever product, the A1, essentially invents an entirely novel subcategory: the 360 drone. Using the same shoot-first, frame-later technology as the Insta360 X5 (Antigravity is technically a distinct company from Insta360, but the brands have close ties), the A1 has twin cameras to capture everything around it, allowing the user to reframe the footage later using mobile or desktop apps. Each of the cameras uses a 1/1.28-inch sensor and an ultrawide lens to capture a hemispherical view.

Transition control poses a critical challenge in Vertical Take-Off and Landing Unmanned Aerial Vehicle (VTOL UAV) development due to the tilting rotor mechanism, which shifts the center of gravity and thrust direction during transitions. Current control methods' decoupled control of altitude and position leads to significant vibration, and limits interaction consideration and adaptability. In this study, we propose a novel coupled transition control methodology based on reinforcement learning (RL) driven controller. Besides, contrasting to the conventional phase-transition approach, the ST3M method demonstrates a new perspective by treating cruise mode as a special case of hover. We validate the feasibility of applying our method in simulation and real-world environments, demonstrating efficient controller development and migration while accurately controlling UAV position and attitude, exhibiting outstanding trajectory tracking and reduced vibrations during the transition process.

Abstract-- This study presents a transformer-based approach for fault-tolerant control in fixed-wing Unmanned Aerial Vehicles (UAVs), designed to adapt in real time to dynamic changes caused by structural damage or actuator failures. Employing a teacher-student knowledge distillation framework, the proposed approach trains a student agent with partial observations by transferring knowledge from a privileged expert agent with full observability, enabling robust performance across diverse failure scenarios. In recent years, Unmanned Aerial Vehicles (UAVs) have been widely used to perform various applications in complex However, complex environments and demanding tasks can and critical scenarios, such as search and rescue or cause structural damage to the UAV, altering its aerodynamic autonomous medical transportation. Fixed-wing UAVs, in particular, and reliability of these aerial robots have become major exhibit highly complex, nonlinear dynamics, which can concerns due to the potential implications of system failures. Unlike other robotics fields, such as manipulation and Although current FCSs are robust, they struggle to maintain humanoid locomotion, where advanced control methods are performance when the vehicle dynamics deviate from the essential for managing complex joint movements, UAV original design specifications, sometimes leading to control Flight Control Systems (FCSs) in industry typically rely divergence and catastrophic failure.

This work briefly covers our efforts to stabilize the flight dynamics of Northeastern's tailless bat-inspired micro aerial vehicle, Aerobat. Flapping robots are not new. A plethora of examples is mainly dominated by insect-style design paradigms that are passively stable. However, Aerobat, in addition for being tailless, possesses morphing wings that add to the inherent complexity of flight control. The robot can dynamically adjust its wing platform configurations during gait cycles, increasing its efficiency and agility. We employ a guard design with manifold small thrusters to stabilize Aerobat's position and orientation in hovering, a flapping system in tandem with a multi-rotor. For flight control purposes, we take an approach based on assuming the guard cannot observe Aerobat's states. Then, we propose an observer to estimate the unknown states of the guard which are then used for closed-loop hovering control of the Guard-Aerobat platform.

Neural network simulations of the dragonfly flight neurocontrol system have been developed to understand how this insect uses complex, unsteady aerodynamics. The simulation networks account for the ganglionic spatial distribution of cells as well as the physiologic operating range and the stochastic cellular fIring history of each neuron. In addition the motor neuron firing patterns, "flight command sequences", were utilized. Simulation training was targeted against both the cellular and flight motor neuron firing patterns. The trained networks accurately resynthesized the intraganglionic cellular firing patterns.

What can fly like a bird and hover like an insect? If drones had this combo, they would be able to maneuver better through collapsed buildings and other cluttered spaces to find trapped victims. Purdue University researchers have engineered flying robots that behave like hummingbirds, trained by machine learning algorithms based on various techniques the bird uses naturally every day. This means that after learning from a simulation, the robot "knows" how to move around on its own like a hummingbird would, such as discerning when to perform an escape maneuver. Artificial intelligence, combined with flexible flapping wings, also allows the robot to teach itself new tricks.

Insects and hummingbirds exhibit extraordinary flight capabilities and can simultaneously master seemingly conflicting goals: stable hovering and aggressive maneuvering, unmatched by small scale man-made vehicles. Flapping Wing Micro Air Vehicles (FWMAVs) hold great promise for closing this performance gap. However, design and control of such systems remain challenging due to various constraints. Here, we present an open source high fidelity dynamic simulation for FWMAVs to serve as a testbed for the design, optimization and flight control of FWMAVs. For simulation validation, we recreated the hummingbird-scale robot developed in our lab in the simulation. System identification was performed to obtain the model parameters. The force generation, open-loop and closed-loop dynamic response between simulated and experimental flights were compared and validated. The unsteady aerodynamics and the highly nonlinear flight dynamics present challenging control problems for conventional and learning control algorithms such as Reinforcement Learning. The interface of the simulation is fully compatible with OpenAI Gym environment. As a benchmark study, we present a linear controller for hovering stabilization and a Deep Reinforcement Learning control policy for goal-directed maneuvering. Finally, we demonstrate direct simulation-to-real transfer of both control policies onto the physical robot, further demonstrating the fidelity of the simulation.

Birds do it but bees don't. I've done it, a little. Of course, I'm talking about piloting a sailplane, which is what soaring aficionados call their craft (almost everyone else calls it a glider). The entire point of a sailplane is to gather its energy from warm rising air currents. Sailplanes have no engines, but they do have the usual aircraft flight controls: rudder, elevator, ailerons, and almost always spoilers.

What does a company do when its trailblazing and diverse innovations for nearly half a century have redefined how the world drives and flies? When its many technological "firsts" include the first practical electric car, flying the Nano Hummingbird drone, record-setting, solar-powered aircraft flights in near space, and reshaping the battlefield with portable, hand-held, tactical drones and loitering munitions? It takes on another world. AeroVironment, Inc. has revealed its critical role in collaborating with NASA's Jet Propulsion Laboratory (NASA/JPL) in Pasadena, Calif. to build the drone helicopter recently selected by NASA/JPL's Mars Exploration Program, and displayed a model of the Mars Helicopter, which is planned to fly on Mars in less than three years. "AeroVironment's deep, rich and diverse history of innovation combined with our experience with near-space aircraft like Pathfinder and Helios make us uniquely suited to collaborate with NASA and JPL on this historic, interplanetary venture," said AeroVironment President and Chief Executive Officer Wahid Nawabi.

This approach consists of defining an upper and lower threshold so that when a measurement goes above the upper limit or below the lower one, an alarm is triggered. We discuss the limitations of the out-of-limits approach and propose a new monitoring paradigm based on novelty detection. The proposed monitoring approach can detect novel behaviors, which are often signatures of anomalies, very early -- allowing engineers in some cases to react before the anomaly develops. A prototype implementing this monitoring approach has been implemented and applied to several ESA missions. The operational assessment from the XMM-Newton operations team is presented.