C. De Wagter

Design, Aerodynamics, and Vision-Based Control of the DelFly

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.

Design, aerodynamics and autonomy of the DelFly

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.

Optic-flow based slope estimation for autonomous landing

Micro Air Vehicles need to have a robust landing capability, especially when they operate outside line-of-sight. Autonomous landing requires the identification of a relatively flat landing surface that does not have too large an inclination. In this article, a vision algorithm is introduced that fits a second-order approximation to the optic flow field underlying the optic flow vectors in images from a bottom camera. The flow field provides information on the ventral flow (Vx/h), the time-to-contact (h/ – Vz), the flatness of the landing surface, and the surface slope. The algorithm is computationally efficient and since it regards the flow field as a whole, it is suitable for use during relatively fast maneuvers. The algorithm is subsequently tested on artificial image sequences, hand-held videos, and on the images made by a Parrot AR drone. In a preliminary robotic experiment, the AR drone uses the vision algorithm to determine when to land in a scenario where it flies off a stairs onto the flat floor.

Sky Segmentation Approach to obstacle avoidance

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.

Flow visualization in the wake of flapping-wing MAV ‘DelFly II’ in forward flight

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.

The appearance variation cue for obstacle avoidance

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.

Error analysis and assessment of unsteady forces acting on a flapping wing micro air vehicle: free flight versus wind-tunnel experimental methods.

An accurate knowledge of the unsteady aerodynamic forces acting on a bio-inspired, flapping-wing micro air vehicle (FWMAV) is crucial in the design development and optimization cycle. Two different types of experimental approaches are often used: determination of forces from position data obtained from external optical tracking during free flight, or direct measurements of forces by attaching the FWMAV to a force transducer in a wind-tunnel. This study compares the quality of the forces obtained from both methods as applied to a 17.4 gram FWMAV capable of controlled flight. A comprehensive analysis of various error sources is performed. The effects of different factors, e.g., measurement errors, error propagation, numerical differentiation, filtering frequency selection, and structural eigenmode interference, are assessed. For the forces obtained from free flight experiments it is shown that a data acquisition frequency below 200 Hz and an accuracy in the position measurements lower than ± 0.2 mm may considerably hinder determination of the unsteady forces. In general, the force component parallel to the fuselage determined by the two methods compares well for identical flight conditions; however, a significant difference was observed for the forces along the stroke plane of the wings. This was found to originate from the restrictions applied by the clamp to the dynamic oscillations observed in free flight and from the structural resonance of the clamped FWMAV structure, which generates loads that cannot be distinguished from the external forces. Furthermore, the clamping position was found to have a pronounced influence on the eigenmodes of the structure, and this effect should be taken into account for accurate force measurements.

Sub-sampling: Real-time vision for micro air vehicles

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.

Tethered vs. free flight force determination of the DelFly II Flapping Wing Micro Air Vehicle

The determination of dynamic forces acting on a Flapping Wing Micro Aerial Vehicle (FWMAV) is a challenging task due to the unsteady nature of force generation mechanisms. To assure a proper force identification in future researches, this work compares two different methods to obtain the longitudinal forces acting on FWMAVs and discusses their applicability regions. The methods were 1) calculation of forces from the recordings of the FWMAVs position in a free flight condition; 2) direct force measurements in a tethered flight condition in a wind tunnel. The DelFly II is used as the FWMAV test platform in the measurements. During free flight experiments, its position and attitude were recorded at a rate of 200Hz using an external visual tracking system, whose acquired information was then analyzed to obtain the flight states and calculate the forces and moments that act on the platform during flight, under a set of kinematic assumptions. Subsequently, similar flight conditions were tested in the tethered situation. An ATI Nano-17 Titanium force transducer was used to measure time-resolved forces. The results for the most common flight regime of the DelFly, which is a slow forward flight at a high body pitch angle, are presented. It is shown that the tethered force balance tests agree with the free flight data when assessing the aerodynamic forces that are perpendicular to the stroke plane of the flapping wing. However, the forces that act along the stroke plane are coupled with structural dynamic terms, thus affecting the final lift and thrust identification. These results point to inadequate force identification in fixed point force measurements, due to effect the of the dynamic modes of the FWMAV body, thus advising proper cross-comparing between experimental methods.

Attitude and altitude estimation and control on board a Flapping Wing Micro Air Vehicle

The autonomous capabilities of light-weight Flapping Wing Micro Air Vehicles (FWMAVs) have much to gain from onboard state estimation and attitude control. In this article, we present the first FWMAV with robust onboard state estimation and attitude control. The tailed FWMAV DelFly II was used, with the main goal to achieve active stabilization in the (passively unstable) hover condition. The attitude is estimated using an Inertial Measurement Unit with a gyroscope, accelerometer and magnetometer and the altitude is estimated using a barometer. A major challenge lies in the disturbance of the accelerometer measurements by the flapping motion of the wings. We propose a mechanical damping mechanism and flap-cycle based filtering to resolve this issue. The pitch estimates have a mean error of 1.5° with respect to the ground-truth measurement from a motion capture system. Using the onboard pitch estimate we can control the attitude of the FWMAV in the forward flight regime with a 30% lower standard deviation than in a trimmed flight. With a different set of gains, the FWMAV is able to perform a hovering flight - showing that a tailed FWMAV has enough control authority for this task. In a fully autonomous hover experiment, the DelFly II stays within a sphere of 0.75 m radius.