Scale-free features in collective robot foraging

In many complex systems observed in nature, properties such as scalability, adaptivity, or rapid information exchange are often accompanied by the presence of features that are scale-free, i.e., that have no characteristic scale. Following this observation, we investigate the existence of scale-free features in artificial collective systems using simulated robot swarms. We implement a large-scale swarm performing the complex task of collective foraging, and demonstrate that several space and time features of the simulated swarm-such as number of communication links or time spent in resting state-spontaneously approach the scale-free property with moderate to strong statistical plausibility. Furthermore, we report strong correlations between the latter observation and swarm performance in terms of the number of retrieved items

Collective sampling of environmental features under limited sampling budget

Exploration of an unknown environment is one of the most prominent tasks for multi-robot systems. In this paper, we focus on the specific problem of how a swarm of simulated robots can collectively sample a particular environment feature. We propose an energy-efficient approach for collective sampling, in which we aim to optimize the statistical quality of the collective sample while each robot is restricted in the number of samples it can take. The individual decision to sample or discard a detected item is performed using a voting process, in which robots vote to converge to the collective sample that reflects best the inter-sample distances. These distances are exchanged in the local neighbourhood of the robot. We validate our approach using physics-based simulations in a 2D environment. Our results show that the proposed approach succeeds in maximizing the spatial coverage of the collective sample, while minimizing the number of taken samples. (C) 2019 Elsevier B.V. All rights reserved

Probabilistic analysis of long-term swarm performance under spatial interferences

Swarm robotics is a branch of collective robotics that outperforms many other systems due to its large number of robots. It allows for performing several tasks that are beyond the capability of a single or multi robot systems. Its global behaviour emerges from the local rules implemented on the level of its individual robots. Thus, estimating the obtained performance in a self-organized manner represents one of the main challenges, especially under complex dynamics like spatial interferences. In this paper, we exploit the central limit theorem (CLT) to analyse and estimate the swarm performance over long-term deadlines and under potential spatial interferences. The developed model is tested on the well-known foraging task, however, it can be generalized to be applied on any constrictive robotic task. © 2013 Springer-Verlag.SCOPUS: cp.kinfo:eu-repo/semantics/publishe