R. Ruijsink

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.

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.

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.

Stereo Vision Based Obstacle Avoidance on Flapping Wing MAVs

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.

Autonomous Wind Tunnel Free-Flight of a Flapping Wing MAV

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.

Design, control, and visual navigation of the DelftaCopter VTOL tail-sitter UAV

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.

Autonomous Flight with Onboard Stereo Vision

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.

Introduction to Flapping Wing Design

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.