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.

We demonstrate a scenario in which a mobile robot, according to a plan, builds a structure that it can then enter. The robot interacts with the construction using local sensing. This synthesis of planning and stigmergy opens the way to new construction techniques using mobile robots.

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.

Design and study of mixed animal-robot societies are the fields of scientific exploration that can bring new opportunities for research into the group behavior of social animals. Our goal is to develop a chicken robot - an autonomous mobile robot, socially acceptable by a group of chicks and able to interact with them using appropriate communication channels. One of the basic requirements to such a robot is the safety of its motion with respect to the chicks, so it has to be endowed with an efficient real-time collision avoidance system. In this paper we present a fuzzy obstacle avoidance system that was designed for the chicken robot using the theory of fuzzy relation equations. This approach allows to easily check a consistency of a used rule base and provides a more systematic approach to design of fuzzy control systems comparing with the classical techniques. The experimental results demonstrate that a mobile robot equipped with the presented system is able to successfully avoid obstacles and safely navigate on an experimental arena.