Tiny, light-weight drones are very suitable for indoor drone applications. They are inherently safe for humans, can navigate even in narrow spaces, while still capturing the information necessary for the application such as warehouse stock tracking or search-and-rescue. For such applications, the tiny drones will have to fly completely by themselves. This is very challenging, since they are very restricted in terms of sensing, processing power, and memory. I will present the research we performed at TU Delft to achieve fully autonomous flight of swarms of tiny drones. First, I will delve into the intelligence of single drones, such as the DelFly Explorer, a flapping wing Micro Air Vehicle that with its 20 grams is still the lightest drone in the world able to fly around and avoid obstacle completely by itself. After that, I will discuss how we achieved to let the drones sense where neighboring drones are. Sensing other robots in a local neighborhood is a very common and fundamental assumption of much of the theoretical work in the area of swarm algorithms, but is very hard to achieve on small flying robots. Finally, I will describe the remaining major challenges on the road towards swarms of small flying robots.
Received his M.Sc. and Ph.D. in the field of Artificial Intelligence (AI) at Maastricht University, the Netherlands. His research interest lies with computationally efficient and often bio-inspired algorithms for robot autonomy, with an emphasis on computer vision. Since 2008 he has worked on algorithms for achieving autonomous flight with small and light-weight flying robots, such as the DelFly flapping wing MAV. In 2011-2012, he was a research fellow in the Advanced Concepts Team of the European Space Agency, where he studied topics such as optical flow based control algorithms for extraterrestrial landing scenarios. Currently, he is associate professor at TU Delft and scientific lead of the Micro Air Vehicle lab (MAV-lab) of Delft University of Technology.