G. C. H. E. de Croon

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.

Time-Varying Model Identification of Flapping-Wing Vehicle Dynamics Using Flight Data

A time-varying model for the forward flight dynamics of a flapping-wing micro aerial vehicle is identified from free-flight optical tracking data. The model is validated and used to assess the validity of the widely applied time-scale separation assumption. Based on this assumption, each aerodynamic force and moment is formulated as a linear addition of decoupled time-averaged and time-varying submodels. The resulting aerodynamic models are incorporated in a set of linearized equations of motion, yielding a simulation-capable full dynamic model. The time-averaged component includes both the longitudinal and the lateral aerodynamics and is assumed to be linear. The time-varying component is modeled as a third-order Fourier series, which approximates the flapping dynamics effectively. Combining both components yields a more complete and realistic simulation. Results suggest that while in steady flight the time-scale separation assumption applies well during maneuvers the time-varying dynamics are not fully ...

Evolutionary robotics approach to odor source localization

An Evolutionary Robotics (ER) approach to the task of odor source localization is investigated. In particular, Continuous Time Recurrent Neural Networks (CTRNNs) are evolved for odor source localization in simulated turbulent odor plumes. In the experiments, the simulated robot is equipped with a single chemical sensor and a wind direction sensor. Three main contributions are made. First, it is shown that the ER approach can be successfully applied to odor source localization in both low-turbulent and high-turbulent conditions. Second, it is demonstrated that a small neural network is able to successfully perform all three sub-tasks of odor source localization: (i) finding the odor plume, (ii) moving toward the odor source, and (iii) identifying the odor source. Third, the analysis of the evolved behaviors reveals two novel odor source localization strategies. These strategies are successfully re-implemented as finite state machines, validating the insights from the analysis of the neural controllers.

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.

Force generation and wing deformation characteristics of a flapping-wing micro air vehicle 'DelFly II' in hovering flight.

The study investigates the aerodynamic performance and the relation between wing deformation and unsteady force generation of a flapping-wing micro air vehicle in hovering flight configuration. Different experiments were performed where fluid forces were acquired with a force sensor, while the three-dimensional wing deformation was measured with a stereo-vision system. In these measurements, time-resolved power consumption and flapping-wing kinematics were also obtained under both in-air and in-vacuum conditions. Comparison of the results for different flapping frequencies reveals different wing kinematics and deformation characteristics. The high flapping frequency case produces higher forces throughout the complete flapping cycle. Moreover, a phase difference occurs in the variation of the forces, such that the low flapping frequency case precedes the high frequency case. A similar phase lag is observed in the temporal evolution of the wing deformation characteristics, suggesting that there is a direct link between the two phenomena. A considerable camber formation occurs during stroke reversals, which is mainly determined by the stiffener orientation. The wing with the thinner surface membrane displays very similar characteristics to the baseline wing, which implies the dominance of the stiffeners in terms of providing rigidity to the wing. Wing span has a significant effect on the aerodynamic efficiency such that increasing the span length by 4 cm results in a 6% enhancement in the cycle-averaged X-force to power consumption ratio compared to the standard DelFly II wings with a span length of 28 cm.

Rigid-Body Kinematics Versus Flapping Kinematics of a Flapping Wing Micro Air Vehicle

Several formulations have been proposed to model the dynamics of ornithopters, with inconclusive results regarding the need for complex kinematic formulations. Furthermore, the impact of assumptions made in the collected results was never assessed by comparing simulations with real flight data. In this study two dynamic models of a Flapping Wing Micro Aerial Vehicle (FWMAV) were derived and compared: a) single rigid body aircraft equations of motion and b) Virtual Work Principle derivation for multiple rigid body flapping kinematics. The aerodynamic forces and moments were compared by feeding the states that were reconstructed from the position and attitude data of a 17 gram free flying FWMAV into the dynamic equations of both formulations. To understand the applicability of rigid body formulations to FWMAVs, six wing-to-body mass ratios and two wing configurations were studied using real flight data. The results show that rigid body models are valid for the aerodynamic reconstruction of FWMAVs with four wings in ‘X’ configuration and two-winged FWMAV with a total wing-to-body mass ratio below 24% and 5.6%, respectively, without considerable information loss.