Alexandre Campo

Analyzing fish movement as a persistent turning walker

The trajectories of Kuhlia mugil fish swimming freely in a tank are analyzed in order to develop a model of spontaneous fish movement. The data show that K. mugil displacement is best described by turning speed and its auto-correlation. The continuous-time process governing this new kind of displacement is modelled by a stochastic differential equation of Ornstein–Uhlenbeck family: the persistent turning walker. The associated diffusive dynamics are compared to the standard persistent random walker model and we show that the resulting diffusion coefficient scales non-linearly with linear swimming speed. In order to illustrate how interactions with other fish or the environment can be added to this spontaneous movement model we quantify the effect of tank walls on the turning speed and adequately reproduce the characteristics of the observed fish trajectories.

Open E-puck Range & Bearing miniaturized board for local communication in swarm robotics

We have designed and built a new open hardware/ software board that lets miniaturized robots communicate and at the same time obtain the range and bearing of the source of emission. The open E-puck Range & Bearing board improves an existing infrared relative localization/communication software library (libIrcom) developed for the e-puck robot and based on its on-board infrared sensors. The board allows the robots to have an embodied, decentralized and scalable communication system. Its use and capabilities are demonstrated via an alignment experiment.

CoCoRo -- The Self-Aware Underwater Swarm

The EU-funded CoCoRo project studies heterogeneous swarms of AUVs used for the purposes of under water monitoring and search. The CoCoRo underwater swarm system will combine bio-inspired motion principles with biologically-derived collective cognition mechanisms to provide a novel robotic system that is scalable, reliable and flexible with respect its behavioural potential. We will investigate and develop swarm-level emergent self-awareness, taking biological inspiration from fish, honeybees, the immune system and neurons. Low-level, local information processing will give rise to collective-level memory and cognition. CoCoRo will develop a novel bio-inspired operating system whose default behaviour will be to provide AUV shoaling functionality and the maintenance of swarm coherence. Collective discrimination of environmental properties will be processed on an individual-or on a collective-level given the cognitive capabilities of the AUVs. We will investigate collective self-recognition through experiments inspired by ethology and psychology, allowing for the quantification of collective cognition.

An Open Localization and Local Communication Embodied Sensor.

In this paper we describe a localization and local communication system which allows situated agents to communicate locally, obtaining at the same time both the range and the bearing of the emitter without the need of any centralized control or any external reference. The system relies on infrared communications with frequency modulation and is composed of two interconnected modules for data and power measurement. Thanks to the open hardware license under which it is released, the research community can easily replicate the system at a low cost and/or adapt it for applications in sensor networks and in robotics.

Efficient multi-foraging in swarm robotics

In the multi-foraging task studied in this paper, a group of robots has to efficiently retrieve two different types of prey to a nest. Robots have to decide when they leave the nest to forage and which prey to retrieve. The goal of this study is to identify an efficient multi-foraging behaviour, where efficiency is defined as a function of the energy that is spent by the robots during exploration and gained when a prey is retrieved to the nest. We design and validate a mathematical model that is used to predict the optimal behaviour. We introduce a decision algorithm and use simulations to study its performance in a wide range of experimental situations with respect to the predictions of the mathematical model.

Collective decision-making based on social odometry

In this paper, we propose a swarm intelligence localization strategy in which robots have to locate different resource areas in a bounded arena and forage between them. The robots have no knowledge of the arena dimensions and of the number of resource areas. The strategy is based on peer-to-peer local communication without the need for any central unit. Social Odometry leads to a self-organized path selection. We show how collective decisions lead the robots to choose the closest resource site from a central place. Results are presented with simulated and real robots.

Self-organized discrimination of resources.

When selecting a resource to exploit, an insect colony must take into account at least two constraints: the resource must be abundant enough to sustain the whole group, but not too large to limit exploitation costs, and risks of conflicts with other colonies. Following recent results on cockroaches and ants, we introduce here a behavioral mechanism that satisfies these two constraints. Individuals simply modulate their probability to switch to another resource as a function of the local density of conspecifics locally detected. As a result, the individuals gather at the smallest resource that can host the whole group, hence reducing competition and exploitation costs while fulfilling the overall groups needs. Our analysis reveals that the group becomes better at discriminating between similar resources as it grows in size. Also, the discrimination mechanism is flexible and the group readily switches to a better suited resource as it appears in the environment. The collective decision emerges through the self-organization of individuals, that is, in absence of any centralized control. It also requires a minimal individual cognitive investment, making the proposed mechanism likely to occur in other social species and suitable for the development of distributed decision making tools.

Shape transition during nest digging in ants

Nest building in social insects is among the collective processes that show highly conservative features such as basic modules (chambers and galleries) or homeostatic properties. Although ant nests share common characteristics, they exhibit a high structural variability, of which morphogenesis and underlying mechanisms remain largely unknown. We conducted two-dimensional nest-digging experiments under homogeneous laboratory conditions to investigate the shape diversity that emerges only from digging dynamics and without the influence of any environmental heterogeneity. These experiments revealed that, during the excavation, a morphological transition occurs because the primary circular cavity evolves into a ramified structure through a branching process. Such a transition is observed, whatever the number of ants involved, but occurs more frequently for a larger number of workers. A stochastic model highlights the central role of density effects in shape transition. These results indicate that nest digging shares similar properties with various physical, chemical, and biological systems. Moreover, our model of morphogenesis provides an explanatory framework for shape transitions in decentralized growing structures in group-living animals.

Fundamental Collective Behaviors in Swarm Robotics

In this chapter, we present and discuss a number of types of fundamental collective behaviors studied within the swarm robotics domain. Swarm robotics is a particular approach to the design and study of multi-robot systems, which emphasizes decentralized and self-organizing behavior that deals with limited individual abilities, local sensing, and local communication. The desired features for a swarm robotics system are flexibility to variable environmental conditions, robustness to failure, and scalability to large groups. These can be achieved thanks to well-designed collective behavior – often obtained via some sort of bio-inspired approach – that relies on cooperation among redundant components. In this chapter, we discuss the solutions proposed for a limited number of problems common to many swarm robotics systems – namely aggregation, synchronization, coordinated motion, collective exploration, and decision making. We believe that many real-word applications subsume one or more of these problems, and tailored solutions can be developed starting from the studies we review in this chapter. Finally, we propose possible directions for future research and discuss the relevant challenges to be addressed in order to push forward the study and the applications of swarm robotics systems.

Improving Social Odometry Robot Networks with Distributed Reputation Systems for Collaborative Purposes

The improvement of odometry systems in collaborative robotics remains an important challenge for several applications. Social odometry is a social technique which confers the robots the possibility to learn from the others. This paper analyzes social odometry and proposes and follows a methodology to improve its behavior based on cooperative reputation systems. We also provide a reference implementation that allows us to compare the performance of the proposed solution in highly dynamic environments with the performance of standard social odometry techniques. Simulation results quantitatively show the benefits of this collaborative approach that allows us to achieve better performances than social odometry.