Many Unmanned Air Vehicle (UAV) applications require vertical take-off and landing and very long-range capabilities. Fixed-wing aircraft need long runways to land, and electric energy is still a bottleneck for helicopters, which are not range efficient. In this paper, we introduce the NederDrone, a hybrid lift, hybrid energy hydrogen-powered UAV that can perform vertical take-off and landings using its 12 propellers while flying efficiently in forward flight thanks to its fixed wings. The energy is supplied from a combination of hydrogen-driven Polymer Electrolyte Membrane fuel-cells for endurance and lithium batteries for high-power situations. The hydrogen is stored in a pressurized cylinder around which the UAV is optimized. This work analyses the selection of the concept, the implemented safety elements, the electronics and flight control and shows flight data including a 3h38 flight at sea while starting and landing from a small moving ship.@en

To participate in the Outback Medical Express UAV Challenge 2016, a vehicle was designed and tested that can autonomously hover precisely, takeoff and land vertically, fly fast forward efficiently, and use computer vision to locate a person and a suitable landing location. The vehicle is a novel hybrid tail‐sitter combining a delta‐shaped biplane fixed‐wing and a conventional helicopter rotor. The rotor and wing are mounted perpendicularly to each other,and the entire vehicle pitches down to transition from hover to fast forward flight where the rotor serves as propulsion. To deliver sufficient thrust in hover while still being efficient in fast forward flight, a custom rotor system was designed. The theoretical design was validated with energy measurements, wind tunnel tests, and application in real‐world missions. A rotor‐head and corresponding control algorithm were developed to allow transitioning flight with the nonconventional rotor dynamics that are caused by the fuselage rotor interaction. Dedicated electronics were designed that meet vehicle needs and comply with regulations to allow safe flight beyond visual line of sight. Vision‐based search and guidance algorithms running on a stereo‐vision fish‐eye camera were developed and tested to locate a person in cluttered terrain never seen before. Flight tests and a competition participation illustrate the applicability of the DelftaCopter concept. @en

Drone racing is becoming a popular e-sport all over the world, and beating the best human drone race pilots has quickly become a new major challenge for artificial intelligence and robotics. In this paper, we propose a novel sensor fusion method called visual model-predictive localization (VML). Within a small time window, VML approximates the error between the model prediction position and the visual measurements as a linear function. Once the parameters of the function are estimated by the RANSAC algorithm, this error model can be used to compensate the prediction in the future. In this way, outliers can be handled efficiently and the vision delay can also be compensated efficiently. Theoretical analysis and simulation results show the clear advantage compared with Kalman filtering when dealing with the occasional large outliers and vision delays that occur in fast drone racing. Flight tests are performed on a tiny racing quadrotor named “Trashcan,” which was equipped with a Jevois smart camera for a total of 72 g. An average speed of 2 m/s is achieved while the maximum speed is 2.6 m/s. To the best of our knowledge, this flying platform is currently the smallest autonomous racing drone in the world, while still being one of the fastest autonomous racing drones.@en

Flight endurance is still a bottleneck for many types of unmanned air vehicle (UAV) applications. While battery technology improves over the years, for flights that last an entire day, batteries are still insufficient. Hydrogen-powered fuel cells offer an interesting alternative but pose stringent requirements on the platform. The required cruise power must be sufficiently low and flying with a pressurized tank poses new safety and shape constraints. This paper proposes a hybrid transitioning UAV that is optimized towards carrying a hydrogen tank and fuel cell. Hover is achieved using 12 redundant propellers connected to a dual Controller Area Network (CAN) bus and dual power supply. Forward flight is achieved using a tandem wing configuration. The tandem wing not only minimizes the required wingspan to minimize perturbations from gusts during hover, but it also handles the very large pitch inertia of the inline pressure tank and fuel cell very well. During forward flight, 8 of the 12 propellers are folded while the tip propellers counteract the tip vortexes. The propulsion is tested on a force balance and the selected fuel cell is tested in the lab. Finally, a prototype is built and tested in-flight using battery power. Stable hover, good transitioning properties, and stable forward flight are demonstrated. @en

Enlarging the flight envelope of aircraft has been a goal since the beginning of aviation. But requirements to fly very fast and to hover are conflicting. During the design of the DelftaCopter, a tail-sitter hybrid UAV with a single large rotor for lift in hover and propulsion in forward flight, the design of the rotor needs to properly balance hovering requirements and fast forward flight requirements. The initial design with a one meter rotor placed too much emphasis on efficiency in hover, while most flights consist of very short periods of hover and very long phases of forward flight. Two new rotor designs and corresponding motors were tested an open jet wind tunnel. The propulsion system was tested from hover conditions to very fast forward flight in search of the most optimal operating point for each condition. The resulting system requires merely more power than the initial rotor in hover while it is capable of much faster forward speeds. The power requirements are shown to be compatible with modern power sources like Lithium-Ion batteries, which form the next step in improving the efficiency of hover-capable fast UAV. @en

This paper presents a modular autopilot framework for Unmanned Aerial Vehicles (UAVs) that addresses the limitations of modern flight controllers. The framework utilizes separate and external subsystems for actuator control and the resolution of the Control Allocation problem. The actuator subsystem, implemented on a Teensy 4.0 microcontroller, incorporates an Incremental Dynamic Inversion Control (INDI) RPM controller, enabling direct control of motor RPM and facilitating the implementation of dynamic inversion-based control laws. The primary flight computer, a Cube Orange, coordinates the system, while an OrangePi 5 singleboard computer serves as a companion computer. Real flight results demonstrate the effectiveness of the framework, highlighting its potential for robust and efficient UAV control. @en