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A lot of UAV applications require vertical take-off and landing (VTOL) combined with very long-range or endurance. Transitioning UAVs have been proposed to combine the VTOL capabilities of helicopters with the efficient long-range flight properties of fixed-wing aircraft. But energy is still a bottleneck for many electric long endurance applications. While solar power technology and battery technology have improved a lot, in rougher conditions they still respectively lack the power or total amount of energy required for many real-world situations. In this paper, we introduce the NederDrone, a hybrid lift, hybrid energy hydrogen-powered UAV which can perform vertical take-off and landings using 12 propellers while flying efficiently in forward flight thanks to its fixed wings. The energy is supplied from a mix of hydrogen-driven fuel-cells to store large amounts of energy and battery power for high power situations. The hydrogen is stored in a pressurized cylinder around which the UAV is optimized. This paper 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, starting and landing on a small moving ship.

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.

Temperature is a ubiquitous environmental factor that affects how species distribute and behave. Different species of Drosophila fruit flies have specific responses to changing temperatures according to their physiological tolerance and adaptability. Drosophila flies also possess a temperature sensing system that has become fundamental to understanding the neural basis of temperature processing in ectotherms. We present here a temperature-controlled arena that permits fast and precise temperature changes with temporal and spatial control to explore the response of individual flies to changing temperatures. Individual flies are placed in the arena and exposed to pre-programmed temperature challenges, such as uniform gradual increases in temperature to determine reaction norms or spatially distributed temperatures at the same time to determine preferences. Individuals are automatically tracked, allowing the quantification of speed or location preference. This method can be used to rapidly quantify the response over a large range of temperatures to determine temperature performance curves in Drosophila or other insects of similar size. In addition, it can be used for genetic studies to quantify temperature preferences and reactions of mutants or wild-type flies. This method can help uncover the basis of thermal speciation and adaptation, as well as the neural mechanisms behind temperature processing.

Temperature influences the physiology and behavior of all organisms. For ectotherms, which lack central temperature regulation, temperature adaptation requires sheltering from or moving to a heat source. As temperature constrains the rate of metabolic reactions, it can directly affect ectotherm physiology and thus behavioral performance. This direct effect is particularly relevant for insects, as their small bodies readily equilibrate with ambient temperature. In fact, models of enzyme kinetics applied to insect behavior predict performance at different temperatures suggesting that thermal physiology governs behavior. However, insects also possess thermosensory neurons critical for locating preferred temperatures, showing cognitive control. This suggests that temperature-related behavior can emerge directly from a physiological effect, indirectly as a consequence of thermosensory processing, or through a combination of both. To separate the roles of thermal physiology and cognitive control, we developed an arena that allows fast temperature changes in time and space, and in which animals' movements are automatically quantified. We exposed wild-type Drosophila melanogaster and thermosensory receptor mutants to a dynamic temperature environment and tracked their movements. The locomotor speed of wild-type flies closely matched models of enzyme kinetics, but the behavior of thermosensory mutants did not. Mutations in thermosensory receptor gene dTrpA1 (Transient Receptor Potential A1) expressed in the brain resulted in a complete lack of response to temperature changes, while mutations in peripheral thermosensory receptor gene Gr28b(D) resulted in a diminished response. We conclude that flies react to temperature through cognitive control, informed by interactions between various thermosensory neurons, the behavioral output of which resembles models of enzyme kinetics.

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.

The appearance variation cue captures the variation in texture in a single image. Its use for obstacle avoidance is based on the assumption that there is less such variation when the camera is close to an obstacle. For videos of approaching frontal obstacles, it is demonstrated that combining the cue with optic flow leads to better performance than using either cue alone. In addition, the cue is successfully used to control the 16-g flapping-wing micro air vehicle DelFly II.

Small robotic systems such as Micro Air Vehicles (MAVs) need to react quickly to their dynamic environments, while having only a limited amount of energy and processing onboard. In this article, sub-sampling of local image samples is investigated as a straightforward and broadly applicable approach to improve the computational efficiency of vision algorithms. In sub-sampling, only a small subset of the total number of samples is processed, leading to a significant reduction of the computational effort at the cost of a slightly lower accuracy. The possibility to change the number of extracted samples is of particular importance to autonomous robots, since it allows the designer to select not only the performance but also the execution frequency of the algorithm. The approach of sub-sampling is illustrated by introducing two novel, computationally efficient algorithms for two tasks relevant to MAVs: WiFi noise detection in camera images and onboard horizon detection for pitch and roll estimation. In the noise detection task, image lines and pixel pairs are sampled, while in the horizon detection task features from local image patches are sampled. For both tasks experiments are performed and the effects of sub-sampling are analyzed. It is demonstrated that even for small images of size 160x120 speed-ups of a factor 14 to 21 are reached, while retaining a sufficient performance for the tasks at hand.

Light-weight, autonomous ornithopters form a promise to observe places that are too small or too dangerous for humans to enter. In this article, we discuss the DelFly project, in which we follow a top-down approach to ever smaller and more autonomous ornithopters. Top-down signifies that the project always focuses on complete flying systems equipped with camera. We give arguments for the approach by explaining which findings on the DelFly I and DelFly II recently led to the development of the DelFly Micro: a 3.07-gram ornithopter carrying a camera and transmitter onboard. These findings concern the design, aerodynamics, and vision-based control of the DelFly. In addition, we identify main obstacles on the road to fly-sized ornithopters.

In this chapter, we first explain the choice for the X-wing configuration of the DelFly. Subsequently, we discuss the evolution of the different mechanical parts used in the DelFly I, II, Micro, and Explorer. Details are given on the used materials.

In this chapter, an overview of the experimental studies focusing on the aerodynamics of the DelFly is given. Force generation mechanisms of the DelFly flapping-wing flight are addressed with particular consideration of the effects of different parameters such as wing geometry and flexibility. Furthermore, state-of-art flow visualization results are presented to give insight into the behavior of flow structures around and in the wake of the flapping wings and to assess their influence on the unsteady forces.

This chapter treats the main choices, issues, and tradeoffs in the design of flapping wing MAVs. In particular, we discuss the implications of different tail and wing configurations, the energy source and various types of actuators. We also show how choices elementary to aircraft design, such as the trade-off between fuel/battery mass and payload mass can have rather large effects at the scale of light-weight flapping wing MAVs.

The DelFly does not use a map of its environment to achieve successful obstacle avoidance. Instead, it uses more direct visual cues to detect obstacles and to turn in the right direction. In this chapter, we investigate how successful such a direct avoidance strategy can be. We introduce a turning logic for the DelFly and test it both in simulation and on the real DelFly II platform, confirming the validity of the approach and identifying some of the challenges still ahead.

We shift our focus to the use of stereo vision for autonomous flight. Stereo vision implies carrying two cameras on board, which adds weight and increases the power consumption. Still, it also allows for instantaneous distance estimates, which is a considerable advantage on a moving (and oscillating) flapping wing MAV. In particular, we explain the onboard stereo vision and control algorithms that allow the 20-g DelFly Explorer to autonomously fly around in unknown environments for as long as its battery lasts.

In this chapter, physical principles of fixed-wing and flapping-wing aerodynamics are introduced briefly with the associated terminology. Furthermore, the main aerodynamic mechanisms which are responsible for the generation of the forces in the flapping-wing flight are described in order to provide the reader with fundamental knowledge before further comprehensive analysis of the DelFly.

This chapter sets the stage for the research on autonomous flight of the DelFly. First, a general introduction is given to artificial intelligence for robotics. This will permit the layman to understand some of the major challenges in creating autonomous robots, and the different solution approaches. Subsequently, the particular tasks and approaches to autonomous flight of Micro Air Vehicles are discussed. Whereas mainstream approaches are now being applied to \(\sim \)1 kg MAVs such as quadrotors, it will become clear that light-weight flapping wing MAVs are best served by a computationally efficient, vision-based approach to autonomous flight. In particular, in the DelFly project we have adopted a purposive vision approach to autonomous flight, in which only the information necessary for the robot’s task is extracted. The main goal of the approach is to allow the DelFly to autonomously explore unknown indoor environments, to which end we complement optical flow with other, appearance based, visual inputs.

This chapter deals with monocular obstacle detection. The visual cue of optic flow is used to determine the time-to-impact to obstacles in the environment. Since the flapping wing motion hampers the determination of optic flow, a complementary, “appearance variation cue” is studied. Combining these visual cues significantly improves detection results.

One of the major challenges in robotics is to develop a fly-like robot that can autonomously fly around in unknown environments. State-of-the-art research on autonomous flight of light-weight flapping wing MAVs uses information such as optic flow and appearance variation extracted from a single camera, and has met with limited success. This paper presents the first study of stereo vision for onboard obstacle detection. Stereo vision provides instantaneous distance estimates making the method less dependent than single camera methods on the camera motions resulting from the flapping. After hardware modifications specifically tuned to use on a flapping wing MAV, the computationally efficient Semi-Global Matching (SGM) algorithm in combination with off-board processing allows for accurate real-time distance estimation. Closed-loop indoor experiments with the flapping wing MAV DelFly II demonstrate the advantage of this technique over the use of optic flow measurements.

Time-resolved velocity field measurements in the wake of the flapping wings of the DelFly II Micro Aerial Vehicle (MAV) in forward flight configuration were obtained by Stereoscopic Particle Image Velocimetry (Stereo-PIV). The PIV measurements were performed at several spanwise planes in the wake of the flapping wings and at a high framing rate to allow a reconstruction of the temporal development of the three dimensional wake structures throughout the flapping cycle. The wake reconstruction was performed by interpolating between the measurement planes through a Kriging interpolation procedure. First, the general wake topology of the DelFly II model is described in conjunction with the behavior of the distinctive flow structures, in particular, tip vortex, trailing edge vortex, and root vortex. Second, the effect of reduced frequency is investigated by changing the flapping frequency. Comparison of the three dimensional wake structures for different cases of reduced frequency reveals major differences in both formation and interaction of vortical structures.

One of the major challenges in robotics is to develop a fly-like robot that can autonomously fly around in unknown environments. In this paper, we discuss the current state of the DelFly project, in which we follow a top-down approach to ever smaller and more autonomous ornithopters. The presented findings concerning the design, aerodynamics and autonomy of the DelFly illustrate some of the properties of the top-down approach, which allows the identification and resolution of issues that also play a role at smaller scales. A parametric variation of the wing stiffener layout produced a 5% more power-efficient wing. An experimental aerodynamic investigation revealed that this could be associated with an improved stiffness of the wing, while further providing evidence of the vortex development during the flap cycle. The presented experiments resulted in an improvement in the generated lift, allowing the inclusion of a yaw rate gyro, pressure sensor and microcontroller onboard the DelFly. The autonomy of the DelFly is expanded by achieving (1) an improved turning logic to obtain better vision-based obstacle avoidance performance in environments with varying texture and (2) successful onboard height control based on the pressure sensor.

The capability to visually discern possible obstacles from the sky would be a valuable asset to a UAV for avoiding both other flying vehicles and static obstacles in its environment. The main contribution of this article is the presentation of a feasible approach to obstacle avoidance based on the segmentation of camera images into sky and non-sky regions. The approach is named the Sky Segmentation Approach (SSA). The central concept is that potentially threatening static obstacles protrude from the horizon line. The main challenge for SSA is automatically interpreting the images robustly enough for use in various environments and fast enough for real-time performance. In order to achieve robust image segmentation, machine learning is applied to a large database of images with many different types of skies. From these images, different types of visual features are extracted, among which most of the features investigated in the literature. In the interest of execution speed and comprehensibility, decision trees are learned to map the feature values at an image location to a classification as sky or non-sky. The learned decision trees are fast enough to allow real-time execution on a Digital Signal Processor: it is run onboard a small UAV at ∼ 30 Hz. Experiments in simulation and preliminary experiments on a small UAV show the potential of SSA for achieving robust obstacle avoidance in urban areas.

A low-cost high performance control system is developed to enable autonomous untethered flight inside a wind tunnel. Such autonomous flight is desirable for aerodynamic experiments on flapping wing MAVs, since fixing the fuselage has been shown to significantly alter wing deformations, air flow and performance on vehicles with a periodically moving fuselage. To obtain autonomous untethered flight, 3D position information is obtained from off-board WiiMote infrared tracking sensors with a total system accuracy of 0.8mm and an update rate of 80Hz in a quarter cubical meter control box. This information is sent to a 1.5 gram onboard autopilot containing communication, inertial measurements as well as onboard infrared tracking of an in-tunnel LED to achieve the high performance control needed to position itself precisely in the wind tunnel flow. Flight tests were performed with the 16 gram flapping wing MAV DelFly II. The achieved control performance is shown to be sufficient for many new research purposes, like researching the influence of a fixed fuselage in flapping wing aerodynamic measurements and obtaining more precise performance characteristics.