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Collective behavior based on self-organization has been shown in group-living animals from insects to vertebrates. These findings have stimulated engineers to investigate approaches for the coordination of autonomous multirobot systems based on self-organization. In this experimental study, we show collective decision-making by mixed groups of cockroaches and socially integrated autonomous robots, leading to shared shelter selection. Individuals, natural or artificial, are perceived as equivalent, and the collective decision emerges from nonlinear feedbacks based on local interactions. Even when in the minority, robots can modulate the collective decision-making process and produce a global pattern not observed in their absence. These results demonstrate the possibility of using intelligent autonomous devices to study and control self-organized behavioral patterns in group-living animals., The two first authors contributed equally to this work, info:eu-repo/semantics/published

Art. no. 4058708, info:eu-repo/semantics/published

5th International Workshop, ANTS 2006, Brussels, Belgium, September 4-7, 2006, info:eu-repo/semantics/published

In this paper, an automatic synthesis methodology based on evolutionary computation is applied to evolve neural controllers for a homogeneous team of miniature autonomous mobile robots. Both feed-forward and recurrent neural networks can be evolved with fixed or variable network topologies. The efficacy of the evolutionary methodology is demonstrated in the framework of a realistic case study on collective robotic inspection of regular structures, where the robots are only equipped with limited local on-board sensing and actuating capabilities. The neural controller solutions generated during evolutions are evaluated in a sensorbased embodied simulation environment with realistic noise. It is shown that the evolutionary algorithms are able to successfully synthesize a variety of novel neural controllers that could achieve performances comparable to a carefully hand-tuned, rule-based controller in terms of both average performance and robustness to noise.

This paper proposes a hybrid PSO/ACO algorithm for hierarchical classification, where the classes to be predicted are arranged in a tree-like hierarchy. The performance of the algorithm is evaluated on a challenging biological data set, involving the hierarchical functional classification of enzymes. The proposed algorithm is compared with an existing PSO for classification, which was also adapted for hierarchical classification.

In this paper we present three scalable, fully distributed, threshold-based algorithms for allocating autonomous embodied workers to a given task whose demand evolves dynamically over time. Individuals estimate the availability of work based solely on local perceptions. The differences among the algorithms lie in the threshold distribution among teammates (homogeneous or heterogeneous team), in the mechanism used for establishing threshold values (fixed, parameter-based or variable, rule-based), and in the sharing (public) or not sharing (private) of demand estimations through local peer-to-peer communication. We tested the algorithms’ efficiency and robustness in a collective manipulation case study concerned with the clustering of initially scattered small objects. The aggregation experiment has been studied at two different experimental levels using a microscopic model and embodied simulations. Results show that teams using a number of active workers dynamically controlled by one of the allocation algorithms achieve similar or better performances in aggregation than those characterized by a constant team size while using on average a considerably reduced number of agents over the whole aggregation process. While differences in efficiency among the algorithms are small, differences in robustness are much more apparent. Threshold variability and peer-to-peer communication appear to be two key mechanisms for improving worker allocation robustness against environmental perturbations.

In this paper, we present a mathematical model of an aggregation experiment carried out using multiple embodied agents in teams of time-varying sizes. The aggregation experiment is concerned with the gathering and the clustering of small objects initially scattered in an enclosed arena. The number of active agents engaged in the aggregation task is varying according to a local, distributed stimulus-response law, similar to the behavior observed in ant colonies. We use a set of differential equations to describe the dynamics of the system at the macroscopic level. We validate the predictions of this model by comparing them to experimental data obtained using a sensor-based embodied simulator. Results show that the proposed approach delivers accurate predictions and constitutes a computationally efficient tool for studying aggregation experiments with groups of constant or variable sizes. The simplicity of the model suggests that it is easily applicable to other aggregation or segregation experiments characterized by different agent capabilities and individual control algorithms.

Distributed learning is the learning process of multiple autonomous agents in a varying environment, where each agent has only partial information about the global task. In this paper, we investigate the influence of different reinforcement signals (local and global) and team diversity (homogeneous and heterogeneous agents) on the learned solutions. We compare the learned solutions with those obtained by systematic search in a simple case study in which pairs of agents have to collaborate in order to solve the task without any explicit communication. The results show that policies which allow teammates to specialize find an adequate diversity of the team and, in general, achieve similar or better performances than policies which force homogeneity. However, in this specific case study, the achieved team performances appear to be independent of the locality or globality of the reinforcement signal.

info:eu-repo/semantics/published

This paper presents an investigation of odor localization by groups of autonomous mobile robots using principles of swarm intelligence. We describe a distributed algorithm by which groups of agents can solve the full odor localization task more efficiently than a single agent. We then demonstrate that a group of real robots under fully distributed control can successfully traverse a real odor plume. Finally, we show that an embodied simulator can faithfully reproduce the real robots experiments and thus can be a useful tool for off-line study and optimization of odor localization in the real world.