Bio-mimetic robots can interact with groups of animals in bio-hybrid systems to study their behaviour by producing calibrated stimuli and by analysing their responses. Integrating a group of robots into a group of animals to mimic their behaviour is challenging, both in terms of robotic hardware design and robot control. In particular, the robots must be able to react in real-time to the animal changes of behaviour. This implies the need to adequately track and identify animal behaviour. In this paper, we present a novel framework to control several bio-mimetic robots to integrate into groups of fish. Our framework is able to track the position of the fish and robots in real-time. The robots are driven by a bio-mimetic model of fish behaviour from the literature. We show that our multi-robot system can successfully integrate into groups of fish with closed-loop interactions between the robots and the fish.

To design, to study, and to control mixed animals-robots societies is a challenging field of scientific exploration that can bring new frameworks to study individual and collective behaviors in animal and mixed robot-animal societies. In the Chicken Robot project we aim at developing a mobile robot, able to collaborate with a group of chicks and to control certain group behaviors. The first research step is to build formal models of relevant animal behaviors by performing ethological experiments. Hence, one of the principal tasks is to design a setup equipped with appropriate monitoring tools. In this paper, we present a toolset for running chick-robot experiments and analyzing results. It includes an autonomous PoulBot robot and an experimental setup, able to autonomously record experimental video and audio data, to detect displacements of chicks and robots, to detect their calling activity and to provide robots with these data. We also present a visual data analysis system to extract behavioral features of individual chicks using the variational Bayesian Gaussian mixture model classification with a particle filters based prediction of future positions of chicks. We show how these tools are currently used to carry out chick-robot experiments, to collect behavioral data and to extract animal behavioral features that allow us to build behavioral models bound to be implemented in the robot.

In this paper, we investigate the global self-aggregation dynamics arising from local decision-based rewiring of an overlay network, used as an abstraction for an autonomic service-oriented architecture. We measure the ability of a selected set of local rules to foster self-organization of what is originally a random graph into a structured network. Scalability issues with respect to the key parameters of system size and diversity are extensively discussed. Conflicting goals are introduced, in the form of a population of nodes actively seeking to acquire neighbours of a type different from their own, resulting in decreased local homogeneity. We show that a ‘secondary’ self-organization process ensues, whereby nodes spontaneously cluster according to their implicit objective. Finally, we introduce dynamic goals by making the preferred neighbour type a function of the local characteristics of a simulated workload. We demonstrate that in this context, an overlay rewiring process based purely on local decisions and interactions can result in efficient load-balancing without central planning. We conclude by discussing the implications of our findings for the design of future distributed applications, the likely influence of other factors and of extreme parameter values on the ability of the system to self-organize and the potential improvements to our framework.

Tracking of miniature robotic platforms involves major challenges in image recognition and data association. We present our 2.5 years effort into developing a platform-independent, easy to use, and robust tracking software SwisTrack, which is tailored to research in swarm robotics and behavioral biology. We demonstrate the software and algorithm's abilities using two case studies, tracking of a swarm of cockroaches, and a swarm-robotic inspection task, while outlining hard problems in tracking and data-association of marker-less objects. Its open, platform-independent architecture, and easy-to-use interfaces (Matlab, Java, and C++), allowing for (distributed) post-processing of trajectory data online, make the software highly adaptive to particular research projects without changes to the source code. SwisTrack will be publicly available shortly under the OSI Adaptive License via SourceForge.net.

Tracking of miniature robotic platforms involves major challenges in image recognition and data association. We present our 3-year effort into developing the platform-independent, easy-to-use, and robust tracking software SwisTrack, which is tailored to research in swarm robotics and behavioral biology. We demonstrate the software and algorithms abilities using two case studies, tracking of a swarm of cockroaches, and a swarm-robotic inspection task, while outlining hard problems in tracking and data-association of marker-less objects. Tracking accuracy of a moving robot with respect to camera noise and the calibration model are calculated experimentally. Its open, platform-independent architecture, and easy-to-use interfaces (Matlabtrade, Javatrade, and C++), allowing for (distributed) post-processing of trajectory data online, make the software highly adaptive to particular research projects without changes to the source code. SwisTrack is publicly available on Sourceforge.net under the OSI Adaptive License and contributions from the robotics and biology community are encouraged

This paper shows how self-mechanisms give rise to complex but predictable and therefore steerable global system behavior in a cooperative computing environment. The operation of and the interactions between a set of networked autonomic devices are simulated. These are used as access points to a number of services, have the ability to accept or delegate execution of the associated tasks, and can adjust their internal state in response to the demand. At initialization, all devices are assigned a random internal state, i.e., there is no correlation between their configuration and the tasks that they are expected to perform. The authors study the emergence of cooperation and find that it spontaneously occurs when specific conditions are met, which allow individual devices to focus on performing a single task, sacrificing their ability to efficiently perform others. A relatively simple model that can be completely and thoroughly analyzed was chosen so as to demonstrate how the methodology developed to study complex adaptive systems in biology can be a powerful tool when planning the deployment of large ensembles of interacting autonomic devices

This paper shows how self-(*) mechanisms give rise to complex but predictable and therefore steerable global system behavior in a cooperative computing environment. The operation of and the interactions between a set of networked autonomic devices are simulated. These are used as access points to a number of services, have the ability to accept or delegate execution of the associated tasks, and can adjust their internal state in response to the demand. At initialization, all devices are assigned a random internal state, i.e., there is no correlation between their configuration and the tasks that they are expected to perform. The authors study the emergence of cooperation and find that it spontaneously occurs when specific conditions are met, which allow individual devices to focus on performing a single task, sacrificing their ability to efficiently perform others. A relatively simple model that can be completely and thoroughly analyzed was chosen so as to demonstrate how the methodology developed to study complex adaptive systems in biology can be a powerful tool when planning the deployment of large ensembles of interacting autonomic devices.