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.

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.

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.

The autonomous capabilities of light-weight Flapping Wing Micro Air Vehicles (FWMAVs) have much to gain from onboard state estimation and attitude control. In this article, we present the first FWMAV with robust onboard state estimation and attitude control. The tailed FWMAV DelFly II was used, with the main goal to achieve active stabilization in the (passively unstable) hover condition. The attitude is estimated using an Inertial Measurement Unit with a gyroscope, accelerometer and magnetometer and the altitude is estimated using a barometer. A major challenge lies in the disturbance of the accelerometer measurements by the flapping motion of the wings. We propose a mechanical damping mechanism and flap-cycle based filtering to resolve this issue. The pitch estimates have a mean error of 1.5° with respect to the ground-truth measurement from a motion capture system. Using the onboard pitch estimate we can control the attitude of the FWMAV in the forward flight regime with a 30% lower standard deviation than in a trimmed flight. With a different set of gains, the FWMAV is able to perform a hovering flight - showing that a tailed FWMAV has enough control authority for this task. In a fully autonomous hover experiment, the DelFly II stays within a sphere of 0.75 m radius.

Autonomous flight of Flapping Wing Micro Air Vehicles (FWMAVs) is a major challenge in the field of robotics, due to their light weight and the flapping-induced body motions. In this article, we present the first FWMAV with onboard vision processing for autonomous flight in generic environments. In particular, we introduce the DelFly `Explorer', a 20-gram FWMAV equipped with a 0.98-gram autopilot and a 4.0-gram onboard stereo vision system. We explain the design choices that permit carrying the extended payload, while retaining the DelFly's hover capabilities. In addition, we introduce a novel stereo vision algorithm, LongSeq, designed specifically to cope with the flapping motion and the desire to attain a computational effort tuned to the frame rate. The onboard stereo vision system is illustrated in the context of an obstacle avoidance task in an environment with sparse obstacles.