Arash Sadeghi Amjadi, Cem Bilaloğlu, Ali Emre Turgut, Seongin Na, Erol Şahin, Tomáš Krajník, Farshad Arvin
Adaptive Behavior

Aggregation, the gathering of individuals into a single group as observed in animals such as birds, bees, and amoeba, is known to provide protection against predators or resistance to adverse environmental conditions for the whole. Cue-based aggregation, where environmental cues determine the location of aggregation, is known to be challenging when the swarm density is low. Here, we propose a novel aggregation method applicable to real robots in low-density swarms. Previously, Landmark-Based Aggregation (LBA) method had used odometric dead-reckoning coupled with visual landmarks and yielded better aggregation in low-density swarms. However, the method’s performance was affected adversely by odometry drift, jeopardizing its application in real-world scenarios. In this article, a novel Reinforcement Learning-based Aggregation method, RLA, is proposed to increase aggregation robustness, thus making aggregation possible for real robots in low-density swarm settings. Systematic experiments conducted in a kinematic-based simulator and on real robots have shown that the RLA method yielded larger aggregates, is more robust to odometry noise than the LBA method, and adapts better to environmental changes while not being sensitive to parameter tuning, making it better deployable under real-world conditions.

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Measurement and entanglement both have a “spooky” nonlocal flavor to them. Now physicists are harnessing that nonlocality to probe the spread of quantum information and control it.

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Joseph T. Lizier, Frank Bauer, Fatihcan M. Atay, and Jürgen Jost

PNAS 120 (37) e2303332120

How does the quality of synchronization between coupled oscillators depend on the network structure that connects them? This question has been at the forefont of studies on the structure–dynamics relationship, one of the most important open problems in complex systems. We present a method to fully and generally relate the structure of a network to its synchronizability, without assumptions made by previous approaches such as symmetric connections. Moreover, we reveal the impact of patterns of network connections among small groups of nodes (motifs) on the whole of network synchronizability. Our results implicate the prevalence of clustered structure such as feedforward and feedback loops as the most important factor in synchronizability.

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ARYO JAMSHIDPEY, MARCO DORIGO, AND MARY KATHERINE HEINRICH

INTELLIGENT COMPUTING 13 Sep 2023 Vol 2 Article ID: 0044 DOI: 10.34133/icomputing.0044

In collective perception, agents sample spatial data and use the samples to agree on some estimate. In this paper, we identify the sources of statistical uncertainty that occur in collective perception and note that improving the accuracy of fully decentralized approaches, beyond a certain threshold, might be intractable. We propose self-organizing hierarchy as an approach to improve accuracy in collective perception by reducing or eliminating some of the sources of uncertainty. Using self-organizing hierarchy, aspects of centralization and decentralization can be combined: robots can understand their relative positions system-wide and fuse their information at one point, without requiring, e.g., a fully connected or static communication network. In this way, multi-sensor fusion techniques that were designed for fully centralized systems can be applied to a self-organized system for the first time, without losing the key practical benefits of decentralization. We implement simple proof-of-concept fusion in a self-organizing hierarchy approach and test it against three fully decentralized benchmark approaches. We test the perceptual accuracy of the approaches for absolute conditions that are uniform time-invariant, time-varying, and spatially nonuniform with high heterogeneity, as well as the scalability and fault tolerance of their accuracy. We show that, under our tested conditions, the self-organizing hierarchy approach is generally more accurate, more consistent, and faster than the other approaches and also that its accuracy is more scalable and comparably fault-tolerant. Under spatially nonuniform conditions, our results indicate that the four approaches are comparable in terms of similarity to the reference samples. In future work, extending these results to additional methods, such as collective probability distribution fitting, is likely to be much more straightforward in the self-organizing hierarchy approach than in the decentralized approaches.

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