Robust attitude control is an essential aspect of research on autonomous flight of flapping wing Micro Air Vehicles. The mechanical solutions by which the necessary control moments are realised come at the price of extra weight and possible loss of aerodynamic efficiency. Stable flight of these vehicles has been shown by several designs using a conventional tail, but also by tailless designs that use active control of the wings. In this study a control mechanism is proposed that provides active control over the wings. The mechanism improves vehicle stability and agility by generation of control moments for roll, pitch and yaw. Its effectiveness is demonstrated by static measurements around all the three axes. Flight test results confirm that the attitude of the test vehicle, including a tail, can be successfully controlled in slow forward flight conditions. Furthermore, the flight envelope is extended with robust hovering and the ability to reverse the flight direction using a small turn space. This capability is very important for autonomous flight capabilities such as obstacle avoidance. Finally, it is demonstrated that the proposed control mechanism allows for tailless hovering flight.

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Autonomous flight of Flapping Wing Micro Air Vehicles (FWMAVs) is a major challenge in the field of robotics, due to their light weight and their flapping-induced body motions. An FWMAV is presented weighing a mere 20 g while all its sensors and processing for autonomous flight are onboard. The navigation is based on a 4-g stereo vision camera with onboard processing. Three basic navigational tasks are demonstrated, namely obstacle avoidance, door traversing and corridor following. The presented combination of sensors and control routines is shown to allow flight in common unprepared environments like corridors and offices. The algorithms do not depend on prior classification or learning of the environment or control logic and work in any unprepared environment with vertical texture. While some failure cases remain, this work forms an important step towards very small autonomous indoor MAV.@en

The development of autonomous lightweight MAVs, capable of navigating in unknown indoor environments, is one of the major challenges in robotics. The complexity of this challenge comes from constraints on weight and power consumption of onboard sensing and processing devices. In this paper, we propose the 'Droplet' strategy, an avoidance strategy based on stereo vision inputs that outperforms reactive avoidance strategies by allowing constant speed maneuvers while being computationally extremely efficient, and which does not need to store previous images or maps. The strategy deals with nonholonomic motion constraints of most fixed and flapping wing platforms, and with the limited field-of-view of stereo camera systems. It guarantees obstacle-free flight in the absence of sensor and motor noise. We first analyze the strategy in simulation, and then show its robustness in real-world conditions by implementing it on a 20-gram flapping wing MAV.

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Many types of drones have emerged over the last decade and new applications in various sectors are announced almost on a daily basis. In scientific literature, small drones are called Micro Air Vehicles (MAVs). Especially very small MAVs will play a significant role in indoor applications, since their small size allows them to navigate in narrow, cluttered environments. At the same time, many indoor applications will benefit from MAVs becoming fully autonomous. That will allow these vehicles to operate in areas that cannot be accessed by humans for various reasons. @en

Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this article we show the first application of the Behavior Tree framework on a real robotic platform using the evolutionary robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behavior over that of the traditional neural network formulation. As a result, the behavior is easier to comprehend and manually adapt when crossing the reality gap from simulation to reality. This functionality is shown by performing real-world flight tests with the 20-g DelFly Explorer flapping wing micro air vehicle equipped with a 4-g onboard stereo vision system. The experiments show that the DelFly can fully autonomously search for and fly through a window with only its onboard sensors and processing. The success rate of the optimized behavior in simulation is 88%, and the corresponding real-world performance is 54% after user adaptation. Although this leaves room for improvement, it is higher than the 46% success rate from a tuned user-defined controller.@en

Obstacle detection by monocular vision is challenging because a single camera does not provide a direct measure for absolute distances to objects. A self-supervised learning approach is proposed that combines a camera and a very small short-range proximity sensor to find the relation between the appearance of objects in camera images and their corresponding distances. The method is efficient enough to run real time on a small camera system that can be carried onboard a lightweight MAV of 19 g. The effectiveness of the method is demonstrated by computer simulations and by experiments with the real platform in flight.@en