Insects are among the most agile natural flyers. Hypotheses on their flight control cannot always be validated by experiments with animals or tethered robots. To this end, we developed a programmable and agile autonomous free-flying robot controlled through bio-inspired motion changes of its flapping wings. Despite being 55 times the size of a fruit fly, the robot can accurately mimic the rapid escape maneuvers of flies, including a correcting yaw rotation toward the escape heading. Because the robot's yaw control was turned off, we showed that these yaw rotations result from passive, translation-induced aerodynamic coupling between the yaw torque and the roll and pitch torques produced throughout the maneuver. The robot enables new methods for studying animal flight, and its flight characteristics allow for real-world flight missions.

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. INTRODUCTIONOne of the goals of research on Micro Air Vehicles (MAVs) is to arrive at fly-sized MAVs that can fly autonomously in complex environments. Such MAVs form a promise for observation tasks in places that are too small or too dangerous for humans to enter. Their small size would allow the MAVs to enter and navigate in narrow spaces, while autonomous flight would allow the MAV to operate at a large distance from its user.The requirements for the MAV described above are legion. For one, it needs to be as light as possible for endurance, while having enough onboard sensors and processing that allow it to navigate autonomously. Moreover, it needs to be able to hover, allowing it to get a "good look" at the object of observation. At the same time, it needs to fly at higher speeds to travel larger distances.It may come as no surprise that in their quest for a fly-sized MAV, researchers draw inspiration from natural systems. For example, flying insects comply with the requirements mentioned above and can thus provide inspiration for solving the engineering problems encountered in the creation of a fly-sized MAV. One of the key properties of systems inspired by flying insects is that they use flapping wing propulsion (they are ornithopters) 1,2,3,4,5,6,7,8,9 . Especially at smaller sizes, this propulsion method produces more lift than fixed wing configurations 10,11,12,13 .Essentially, there are two main approaches to creating small autonomous ornithopters: bottom-up and top-down. In the bottom-up approach, one starts by creating all the tiny parts that are deemed important to a fly-sized ornithopter 2,14,15 . The most remarkable example of this approach is the work of the Harvard Microrobotics Laboratory. They succeeded in creating a 60 mg robotic insect, which can produce sufficient lift to take off vertically. To achieve this, they made use of Smart Composite Microstructures (SCM) 2 . The robotic insect was still fixed to taut guide wires that restricted the robot to vertical motion and provided both energy and control. In future work, the group plans to allow all degrees of freedom and to incorporate onboard energy supply, sensors, and processing.In the top-down approach, one starts with a fully functioning (relatively large-scale) ornithopter. By studying this ornit...

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.

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This paper presents an approach to the system identification of the Delfly II Flapping Wing Micro Air Vehicle (FWMAV) using flight test data. It aims at providing simple FWMAV aerodynamic models that can be used in simulations as well as in nonlinear flight control systems. The undertaken methodology builds on normal aircraft system identification methods and extends these with techniques that are specific to FWMAV model identification. The entire aircraft model identification cycle is discussed covering the set-up and automatic execution of the flight test experiments, the aircraft states, the aerodynamic forces and moments' reconstruction, the aerodynamic model structure selection, the parameter estimation and finally, the model validation. In particular, a motion capturing facility was used to record the flapper's position in time and from there compute the states and aerodynamic forces and moments that acted on it, assuming flapaveraged dynamics and linear aerodynamic model structures. It is shown that the approach leads to aerodynamic models that can predict the aerodynamic forces with high accuracy. Despite less accurate, the predictions of the aerodynamic moments still follow the general trend of the measured moments. Dynamic simulations based on the identified aerodynamic models show flight trajectories that closely match the ground truth spanning a number of flapping cycles. Finally, the dimensional aerodynamic forces and moments' coefficients of two of the identified aerodynamic models are presented.