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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.

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.

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.

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.