We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. The numerous existing flocking models are rarely tested on actual hardware because they typically neglect some crucial aspects of multirobot systems. Constrained motion and communication capabilities, delays, perturbations, or the presence of barriers should be modeled and treated explicitly because they have large effects on collective behavior during the cooperation of real agents. Handling these issues properly results in additional model complexity and a natural increase in the number of tunable parameters, which calls for appropriate optimization methods to be coupled tightly to model development. In this paper, we propose such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 30 drones. This is the largest of such aerial outdoor systems without central control reported to date exhibiting flocking with collective collision and object avoidance. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones.
Optimized flocking of autonomous drones in confined environments
Gábor Vásárhelyi, Csaba Virágh, Gergő Somorjai, Tamás Nepusz, Agoston E. Eiben and Tamás Vicsek
Science Robotics 18 Jul 2018:
Vol. 3, Issue 20, eaat3536
In this paper we argue that a rigorous understanding of the nature and implications of complexityreveals that the underlying assumptions that inform our understanding of complex phenomena are deeply related to general philosophical issues. We draw on a very specific philosophical interpretation of complexity, as informed by the work of Paul Cilliers and Edgar Morin. This interpretation of complexity, we argue, resonates with specific themes in post-structural philosophy in general, and deconstruction in particular. We argue that post-structural terms such as différance carry critical insights into furthering our understanding of complexity. The defining feature that distinguishes the account of complexity offered here to other contemporary theories of complexity is the notion of critique. The critical imperative that can be located in a philosophical interpretation of complexity exposes the limitations of totalising theories and subsequently calls for examining the normativity inherent in the knowledge claims that we make. The conjunction of complexity and post-structuralism inscribes a critical-emancipatory impetus into the complexity approach that is missing from othertheories of complexity. We therefore argue for the importance of critical complexity against reductionist or restricted understandings of complexity.
General complexity: A philosophical and critical perspective. Emergence: Complexity and Organization.
Rika Preiser, Minka Woermann, Oliver Human
Recent seminal works on human mobility have shown that individuals constantly exploit a small set of repeatedly visited locations1,2,3. A concurrent study has emphasized the explorative nature of human behaviour, showing that the number of visited places grows steadily over time4,5,6,7. How to reconcile these seemingly contradicting facts remains an open question. Here, we analyse high-resolution multi-year traces of ~40,000 individuals from 4 datasets and show that this tension vanishes when the long-term evolution of mobility patterns is considered. We reveal that mobility patterns evolve significantly yet smoothly, and that the number of familiar locations an individual visits at any point is a conserved quantity with a typical size of ~25. We use this finding to improve state-of-the-art modelling of human mobility4,8. Furthermore, shifting the attention from aggregated quantities to individual behaviour, we show that the size of an individual’s set of preferred locations correlates with their number of social interactions. This result suggests a connection between the conserved quantity we identify, which as we show cannot be understood purely on the basis of time constraints, and the ‘Dunbar number’9,10 describing a cognitive upper limit to an individual’s number of social relations. We anticipate that our work will spark further research linking the study of human mobility and the cognitive and behavioural sciences.
Evidence for a conserved quantity in human mobility
Laura Alessandretti, Piotr Sapiezynski, Vedran Sekara, Sune Lehmann & Andrea Baronchelli
Nature Human Behaviour volume 2, pages 485–491 (2018)
Models developed for gross domestic product (GDP) growth forecasting tend to be extremely complex, relying on a large number of variables and parameters. Such complexity is not always to the benefit of the accuracy of the forecast. Economic complexity constitutes a framework that builds on methods developed for the study of complex systems to construct approaches that are less demanding than standard macroeconomic ones in terms of data requirements, but whose accuracy remains to be systematically benchmarked. Here we develop a forecasting scheme that is shown to outperform the accuracy of the five-year forecast issued by the International Monetary Fund (IMF) by more than 25% on the available data. The model is based on effectively representing economic growth as a two-dimensional dynamical system, defined by GDP per capita and ‘fitness’, a variable computed using only publicly available product-level export data. We show that forecasting errors produced by the method are generally predictable and are also uncorrelated to IMF errors, suggesting that our method is extracting information that is complementary to standard approaches. We believe that our findings are of a very general nature and we plan to extend our validations on larger datasets in future works.
A dynamical systems approach to gross domestic product forecasting
A. Tacchella, D. Mazzilli & L. Pietronero
Nature Physicsvolume 14, pages 861–865 (2018)
Studies of affect labeling, i.e. putting your feelings into words, indicate that it can attenuate positive and negative emotions. Here we track the evolution of individual emotions for tens of thousands of Twitter users by analyzing the emotional content of their tweets before and after they explicitly report having a strong emotion. Our results reveal how emotions and their expression evolve at the temporal resolution of one minute. While the expression of positive emotions is preceded by a short but steep increase in positive valence and followed by short decay to normal levels, negative emotions build up more slowly, followed by a sharp reversal to previous levels, matching earlier findings of the attenuating effects of affect labeling. We estimate that positive and negative emotions last approximately 1.25 and 1.5 hours from onset to evanescence. A separate analysis for male and female subjects is suggestive of possible gender-specific differences in emotional dynamics.
Does putting your emotions into words make you feel better? Measuring the minute-scale dynamics of emotions from online data
Rui Fan, Ali Varamesh, Onur Varol, Alexander Barron, Ingrid van de Leemput, Marten Scheffer, Johan Bollen