Frank Bonnet

A miniature mobile robot developed to be socially integrated with species of small fish

A robot accepted by animals as conspecific is a very powerful tool in behavioral biology, particularly in studies of gregarious animals. In this paper we present the first results of acceptance of a robotic fish designed for experiments on collective animal behavior. The robot consists of two modules: a fish lure fixed on a magnetic base and a miniature mobile robot guiding the lure from below the experimental tank. In order to study the acceptance of the robot among living fish, we varied several parameters of the system and used design of experiments methods to reduce the number of performed experiments and determined the impact of each factor on the acceptance of the robot among a small group of real fish. While a brief comparison of the mean distance of the fish to the robot tends to indicate that the fish are attracted by the lure, a linear model of the acceptance of the robot is presented. Results of this study can be used to improve the design and control of the robot for further animal-robot interaction experiments.

Design of a modular robotic system that mimics small fish locomotion and body movements for ethological studies

Robotic animals are nowadays developed for various types of research, such as bioinspired robotics, biomimetics, and animal behavioral studies. The design of these robots poses great challenges as they often have to achieve very high-level performances in terms of locomotion, size, and visual aspect. We developed a robotic system for direct underwater interactions with small fish species. This robotic platform is composed of two subsystems: a miniature wheeled mobile robot that can achieve complex locomotion patterns and a robotic fish lure that is able to beat its soft caudal peduncle to generate fish-like body movements. The two subsystems are coupled with magnets that allow the robotic lure to reach very high speeds and accelerations, thanks to the mobile robot. We used zebrafish (Danio rerio) to model small fish locomotion patterns and construct a controller for the motion of our robotic system. We have demonstrated that the designed system is able to achieve the same types of motion patterns as the zebrafish while mimicking the body movements of the fish. These results define new standards for robotic fish lures and bring to the field of fish–robot interaction a new tool for ethological studies.

Closed-loop interactions between a shoal of zebrafish and a group of robotic fish in a circular corridor

Collective behavior based on self-organization has been observed in populations of animals from insects to vertebrates. These findings have motivated engineers to investigate approaches to control autonomous multi-robot systems able to reproduce collective animal behaviors, and even to collectively interact with groups of animals. In this article, we show collective decision making by a group of autonomous robots and a group of zebrafish, leading to a shared decision about swimming direction. The robots can also modulate the collective decision-making process in biased and non-biased experimental setups. These results demonstrate the possibility of creating mixed societies of vertebrates and robots in order to study or control animal behavior.

Multi-robot control and tracking framework for bio-hybrid systems with closed-loop interaction

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.

Social Adaptation of Robots for Modulating Self-Organization in Animal Societies

The goal of the work presented here is to influence the overall behaviour of specific animal societies by integrating computational mechatronic devices (robots) into those societies. To do so, these devices should be accepted by the animals aspart of the society and/or as part of the collectively formed environment. For that, we have developed two sets of robotic hardware for integrating into societies of two different animals: zebra fish and young honeybees. We also developed mechanisms to provide feedback from the behaviours of societies for the controllers of the robotic system. Two different computational methods are then used as the controllers of the robots in simulation and successfully adapted by evolutionary algorithms to influence the simulated animals for desired behaviours. Together, these advances in mechatronic hardware, feedback mechanisms, and controller methodology are laying essential foundations to facilitate experiments on modulating self-organised behaviour in mixed animal -- robot societies.

Automated Calibration of a Biomimetic Space-Dependent Model for Zebrafish and Robot Collective Behaviour in a Structured Environment

Animal and robotic collective behaviours can exhibit complex dynamics that require multi-level descriptions. Here, we are interested in developing a multi-level modeling framework for the use of robots in studies about animal collective decision-making. In this context, using robots can be useful for validating models in silico, inducing calibrated repetitive stimuli to trigger animal responses or modulating and controlling animal collective behaviour. However, designing appropriate biomimetic robotic behaviour faces a major challenge: how to go from the collective decision dynamics observed with animals to an actual algorithmic implementation in robots. In previous work, this was mainly done by hand, often by taking inspiration from human-designed models. Typically, models of behaviour are either macroscopic (differential equations of the population dynamics) or microscopic (explicit spatio-temporal state of each individual). Only microscopic models can easily be implemented as robot controllers. Here, we address the problem of automating the design of lower level description models that can be implemented in robots and exhibit the same collective dynamics as a given higher level model. We apply evolutionary algorithms to simultaneously optimise the parameters of models accounting for different levels of description. This methodology is applied to an experimentally validated shelterselection problem solved by gregarious insects and robots. We successfully design and calibrate automatically both a microscopic and a hybrid model exhibiting the same dynamics as a macroscopic one. Our framework can be used for multi-level modeling of collective behaviour in animal or robot populations and bio-hybrid systems.Bio-hybrid systems made of robots and animals can be useful tools both for biology and robotics. To socially integrate robots into animal groups the robots should behave in a biomimetic manner with close loop interactions between robots and animals. Behavioural zebrafish experiments show that their individual behaviours depend on social interactions producing collective behaviour and depend on their position in the environment. Based on those observations we build a multilevel model to describe the zebrafish collective behaviours in a structured environment. Here, we present this new model segmented in spatial zones that each corresponds to different behavioural patterns. We automatically fit the model parameters for each zone to experimental data using a multi-objective evolutionary algorithm. We then evaluate how the resulting calibrated model compares to the experimental data. The model is used to drive the behaviour of a robot that has to integrate socially in a group of zebrafish. We show experimentally that a biomimetic multilevel and context-dependent model allows good social integration of fish and robots in a structured environment.

Infiltrating the zebrafish swarm: design, implementation and experimental tests of a miniature robotic fish lure for fish---robot interaction studies

Robotic fish are nowadays developed for various types of research, such as bio-inspiredrobotics, biomimetics and animal behavior studies. In the context of our research on the social interactions of the zebrafish Danio Rerio, we developed a miniature robotic fish lure for direct underwater interaction with the living fish. This remotely controlled and waterproof device has a total length of 7.5 cm with the same size ratio as zebrafish and is able to beat its tail with different frequencies and amplitudes, while following the group of living animals using a mobile robot moving outside water that is coupled with the robotic lure using magnets. The robotic lure is also equipped with a rechargeable battery and can be used autonomously underwater for experiments of up to 1 h. We performed experiments with the robot moving inside an aquarium with living fish to analyze its impact on the zebrafish behavior. We found that the beating rate of the tail increased the attractiveness of the lure among the zebrafish shoal. We also demonstrated that the lure could influence a collective decision of the zebrafish shoal, the swimming direction, when moving with a constant linear speed inside a circular corridor. This new robotic fish design and the experimental results are promising for the field of fish–robot interaction.

How mimetic should a robotic fish be to socially integrate into zebrafish groups

Biomimetic robots are promising tools in animal behavioural studies. If they are socially integrated in a group of animals, they can produce calibrated social stimuli to test the animal responses. However, the design of such social robots is challenging as it involves both a luring capability including appropriate robot behaviours, and the acceptation of the robots by the animals as social companions. Here, we investigate the integration of a biomimetic robot driven by biomimetic behavioural models into a group of zebrafish (Danio rerio). The robot behaviours are based on a stochastic model linking zebrafish visual perception to individual behaviour and calibrated experimentally to correspond to the behaviour of zebrafish. We show that our robot can be integrated into a group of zebrafish, mimic their behaviour and exhibit similar collective dynamics compared to fish-only groups. This study shows that an autonomous biomimetic robot was enhanced by a biomimetic behavioural model so that it can socially integrate into groups of fish.

Design methods for miniature underwater soft robots

A robotic fish accepted by animals as conspecifics is a very powerful tool in behavioral biology. To obtain the acceptance of the device towards the animals, it should generate some visual stimuli that can be perceived by a living fish as attractive signals. In this paper we introduce a novel design of a robotic fish lure based on a Rigid-Flex Printed Circuit Board (PCB) for experiments on the fish collective behavior. The 3D scan of a zebrafish Danio Rerio, a model animal for fish behavior studies, was used to mimick the main visual features of the zebrafish on the design. The robot can generate different stimuli thanks to its actuated caudal peduncle and two Light-Emitting Diodes (LEDs) placed near its eyes. The prototype has a total length of 63 mm and is only 1.5 times bigger than a zebrafish. The final prototype is waterproof and functional and thus satisfies the necessary conditions for the next steps that will be interaction experiments between the device and living zebrafish.

How to Blend a Robot Within a Group of Zebrafish: Achieving Social Acceptance Through Real-Time Calibration of a Multi-level Behavioural Model

We have previously shown how to socially integrate a fish robot into a group of zebrafish thanks to biomimetic behavioural models. The models have to be calibrated on experimental data to present correct behavioural features. This calibration is essential to enhance the social integration of the robot into the group. When calibrated, the behavioural model of fish behaviour is implemented to drive a robot with closed-loop control of social interactions into a group of zebrafish. This approach can be useful to form mixed-groups, and study animal individual and collective behaviour by using biomimetic autonomous robots capable of responding to the animals in long-standing experiments. Here, we show a methodology for continuous real-time calibration and refinement of multi-level behavioural model. The real-time calibration, by an evolutionary algorithm, is based on simulation of the model to correspond to the observed fish behaviour in real-time. The calibrated model is updated on the robot and tested during the experiments. This method allows to cope with changes of dynamics in fish behaviour. Moreover, each fish presents individual behavioural differences. Thus, each trial is done with naive fish groups that display behavioural variability. This real-time calibration methodology can optimise the robot behaviours during the experiments. Our implementation of this methodology runs on three different computers that perform individual tracking, data-analysis, multi-objective evolutionary algorithms, simulation of the fish robot and adaptation of the robot behavioural models, all in real-time.