Abstract-we present the first decentralized multi-copter flock that performs stable autonomous outdoor flight with up to 10 flying agents. By decentralized and autonomous we mean that all members navigate themselves based on the dynamic information received from other robots in the vicinity. We do not use central data processing or control; instead, all the necessary computations are carried out by miniature on-board computers. The only global information the system exploits is from GPS receivers, while the units use wireless modules to share this positional information with other flock members locally. Collective behavior is based on a decentralized control framework with bio-inspiration from statistical physical modelling of animal swarms. In addition, the model is optimized for stable group flight even in a noisy, windy, delayed and error-prone environment. Using this framework we successfully implemented several fundamental collective flight tasks with up to 10 units: i) we achieved self-propelled flocking in a bounded area with self-organized object avoidance capabilities and ii) performed collective target tracking with stable formation flights (grid, rotating ring, straight line). With realistic numerical simulations we demonstrated that the local broadcast-type communication and the decentralized autonomous control method allows for the scalability of the model for much larger flocks.

Abstract. Animal swarms displaying a variety of typical flocking patterns would not exist without underlying safe, optimal and stable dynamics of the individuals. The emergence of these universal patterns can be efficiently reconstructed with agent-based models. If we want to reproduce these patterns with artificial systems, such as autonomous aerial robots, agent-based models can also be used in the control algorithm of the robots. However, finding the proper algorithms and thus understanding the essential characteristics of the emergent collective behaviour of robots requires the thorough and realistic modeling of the robot and the environment as well. In this paper, first, we present an abstract mathematical model of an autonomous flying robot. The model takes into account several realistic features, such as time delay and locality of the communication, inaccuracy of the onboard sensors and inertial effects. We present two decentralized control algorithms. One is based on a simple self-propelled flocking model of animal collective motion, the other is a collective target tracking algorithm. Both algorithms contain a viscous friction-like term, which aligns the velocities of neighbouring agents parallel to each other. We show that this term can be essential for reducing the inherent instabilities of such a noisy and delayed realistic system. We discuss simulation results about the stability of the control algorithms, and perform real experiments to show the applicability of the algorithms on a group of autonomous quadcopters. Bio-inspiration works in our case two-ways. On the one hand, the whole idea of trying to build and control a swarm of robots comes from the observation that birds tend to flock to optimize their behaviour as a group. On the other hand, by using a realistic simulation framework and studying the group behaviour of autonomous robots we can learn about the major factors influencing the flights of bird flocks.

We have developed an experimental setup of very simple self-propelled robots to observe collective motion emerging as a result of inelastic collisions only. A circular pool and commercial RC boats were the basis of our first setup, where we demonstrated that jamming, clustering, disordered and ordered motion are all present in such a simple experiment and showed that the noise level has a fundamental role in the generation of collective dynamics. Critical noise ranges and the transition characteristics between the different collective patterns were also examined. In our second experiment we used a real-time tracking system and a few steerable model boats to introduce intelligent leaders into the flock. We demonstrated that even a very small portion of guiding members can determine group direction and enhance ordering through inelastic collisions. We also showed that noise can facilitate and speed up ordering with leaders. Our work was extended with an agent-based simulation model, too, and high similarity between real and simulation results were observed. The simulation results show clear statistical evidence of three states and negative correlation between density and ordered motion due to the onset of jamming. Our experiments confirm the different theoretical studies and simulation results in the literature about collision-based, noise-dependent and leader-driven self-propelled particle systems. PACS: 05.70.Fh, 05.65.+b, 89.75.Fb Simulation resultsSimilarly to the experiments, the simulations reveal ordered motion in one (with leaders) or in both directions (without leaders) for low noise levels (figure 14).

We propose a fleet of nanosatellites to perform an all-sky monitoring and timing based localisation of gamma-ray transients. The fleet of at least nine 3U cubesats shall be equipped with large and thin CsI(Tl) scintillator based soft gamma-ray detectors read out by multi-pixel photon counters. For bright short gamma-ray bursts (GRBs), by cross-correlating their light curves, the fleet shall be able to determine the time difference of the arriving GRB signal between the satellites and thus determine the source position with an accuracy of ∼ 10 . This requirement demands precise time synchronization and accurate time stamping of the detected gamma-ray photons, which will be achieved by using on-board GPS receivers. Rapid follow up observations at other wavelengths require the capability for fast, nearly simultaneous downlink of data using a global inter-satellite communication network. In terms of all-sky coverage, the proposed fleet will outperform all GRB monitoring missions.

A fleet of nanosatellites using precise timing synchronization provided by the Global Positioning System is a new concept for monitoring the gamma-ray sky that can achieve both all-sky coverage and good localization accuracy. We are proposing this new concept for the mission CubeSats Applied for MEasuring and LOcalising Transients (CAMELOT). The differences in photon arrival times at each satellite are to be used for source localization. Detectors with good photon statistics and the development of a localization algorithm capable of handling a large number of satellites are both essential for this mission. Large, thin CsI scintillator plates are the current candidates for the detectors because of their high light yields. It is challenging to maximize the light-collection efficiency and to understand the position dependence of such thin plates. We have found a multi-channel readout that uses the coincidence technique to be very effective in increasing the light output while keeping a similar noise level to that of a single channel readout. Based on such a detector design, we have developed a localization algorithm for this mission and have found that we can achieve a localization accuracy better than 20 arc minutes and a rate of about 10 short gamma-ray bursts per year.