Attitude control is an essential flight capability. Whereas flying robots commonly rely on accelerometers¹ for estimating attitude, flying insects lack an unambiguous sense of gravity^2,3. Despite the established role of several sense organs in attitude stabilization^3–5, the dependence of flying insects on an internal gravity direction estimate remains unclear. Here we show how attitude can be extracted from optic flow when combined with a motion model that relates attitude to acceleration direction. Although there are conditions such as hover in which the attitude is unobservable, we prove that the ensuing control system is still stable, continuously moving into and out of these conditions. Flying robot experiments confirm that accommodating unobservability in this manner leads to stable, but slightly oscillatory, attitude control. Moreover, experiments with a bio-inspired flapping-wing robot show that residual, high-frequency attitude oscillations from flapping motion improve observability. The presented approach holds a promise for robotics, with accelerometer-less autopilots paving the road for insect-scale autonomous flying robots⁶. Finally, it forms a hypothesis on insect attitude estimation and control, with the potential to provide further insight into known biological phenomena^5,7,8 and to generate new predictions such as reduced head and body attitude variance at higher flight speeds⁹.

This paper proposes an integral approach for accurate ultra-wideband indoor position control of flapping-wing micro-air vehicles. Three aspects are considered to achieve a reliable and accurate position controller. The first aspect is a velocity/attitude flapping-wing model for drag compensation. The model is compared with real flight data and shown to be applicable for more than one type of flapping-wing drone. The second improvement regards a voltage-dependent thrust control. Lastly, a characterisation of ground effects in flapping-wing flight is obtained from hovering experiments. The proposed controller improves position control by a factor ∼1.5, reaching a mean absolute error of 10cm for the position in x and y, and 4.9cm for the position in z.

In the field of robotics, a major challenge is achieving high levels of autonomy with small vehicles that have limited mass and power budgets. The main motivation for designing such small vehicles is that compared to their larger counterparts, they have the potential to be safer, and hence be available and work together in large numbers. One of the key components in micro robotics is efficient software design to optimally utilize the computing power available. This paper describes the computer vision and control algorithms used to achieve autonomous flight with the ∼30g tailless flapping wing robot, used to participate in the International Micro Air Vehicle Conference and Competition (IMAV 2018) indoor microair vehicle competition. Several tasks are discussed: line following, circular gate detection and fly through. The emphasis throughout this paper is on augmenting traditional techniques with the goal to make these methods work with limited computing power while obtaining robust behavior.

The goal of this paper is to analyse the mechanical resonance that appears during the oscillatory motion in the flapping wing robots. The prototype of the actuation mechanism has been proposed that involves a DC motor directly driving a set of bioinspired wings. The resulting motion has been analysed using a high speed camera.