70 research outputs found
Evolutionary optimisation of neural network models for fish collective behaviours in mixed groups of robots and zebrafish
Animal and robot social interactions are interesting both for ethological
studies and robotics. On the one hand, the robots can be tools and models to
analyse animal collective behaviours, on the other hand, the robots and their
artificial intelligence are directly confronted and compared to the natural
animal collective intelligence. The first step is to design robots and their
behavioural controllers that are capable of socially interact with animals.
Designing such behavioural bio-mimetic controllers remains an important
challenge as they have to reproduce the animal behaviours and have to be
calibrated on experimental data. Most animal collective behavioural models are
designed by modellers based on experimental data. This process is long and
costly because it is difficult to identify the relevant behavioural features
that are then used as a priori knowledge in model building. Here, we want to
model the fish individual and collective behaviours in order to develop robot
controllers. We explore the use of optimised black-box models based on
artificial neural networks (ANN) to model fish behaviours. While the ANN may
not be biomimetic but rather bio-inspired, they can be used to link perception
to motor responses. These models are designed to be implementable as robot
controllers to form mixed-groups of fish and robots, using few a priori
knowledge of the fish behaviours. We present a methodology with multilayer
perceptron or echo state networks that are optimised through evolutionary
algorithms to model accurately the fish individual and collective behaviours in
a bounded rectangular arena. We assess the biomimetism of the generated models
and compare them to the fish experimental behaviours.Comment: 10 pages, 4 figure
Automatic Calibration of Artificial Neural Networks for Zebrafish Collective Behaviours using a Quality Diversity Algorithm
During the last two decades, various models have been proposed for fish
collective motion. These models are mainly developed to decipher the biological
mechanisms of social interaction between animals. They consider very simple
homogeneous unbounded environments and it is not clear that they can simulate
accurately the collective trajectories. Moreover when the models are more
accurate, the question of their scalability to either larger groups or more
elaborate environments remains open. This study deals with learning how to
simulate realistic collective motion of collective of zebrafish, using
real-world tracking data. The objective is to devise an agent-based model that
can be implemented on an artificial robotic fish that can blend into a
collective of real fish. We present a novel approach that uses Quality
Diversity algorithms, a class of algorithms that emphasise exploration over
pure optimisation. In particular, we use CVT-MAP-Elites, a variant of the
state-of-the-art MAP-Elites algorithm for high dimensional search space.
Results show that Quality Diversity algorithms not only outperform classic
evolutionary reinforcement learning methods at the macroscopic level (i.e.
group behaviour), but are also able to generate more realistic biomimetic
behaviours at the microscopic level (i.e. individual behaviour).Comment: 8 pages, 4 figures, 1 tabl
Context Awareness in Swarm Systems
Recent swarms of Uncrewed Systems (UxS) require substantial human input to support their operation. The little 'intelligence' on these platforms limits their potential value and increases their overall cost. Artificial Intelligence (AI) solutions are needed to allow a single human to guide swarms of larger sizes. Shepherding is a bio-inspired swarm guidance approach with one or a few sheepdogs guiding a larger number of sheep. By designing AI-agents playing the role of sheepdogs, humans can guide the swarm by using these AI agents in the same manner that a farmer uses biological sheepdogs to muster sheep. A context-aware AI-sheepdog offers human operators a smarter command and control system. It overcomes the current limiting assumption in the literature of swarm homogeneity to manage heterogeneous swarms and allows the AI agents to better team with human operators.
This thesis aims to demonstrate the use of an ontology-guided architecture to deliver enhanced contextual awareness for swarm control agents. The proposed architecture increases the contextual awareness of AI-sheepdogs to improve swarm guidance and control, enabling individual and collective UxS to characterise and respond to ambiguous swarm behavioural patterns. The architecture, associated methods, and algorithms advance the swarm literature by allowing improved contextual awareness to guide heterogeneous swarms. Metrics and methods are developed to identify the sources of influence in the swarm, recognise and discriminate the behavioural traits of heterogeneous influencing agents, and design AI algorithms to recognise activities and behaviours. The proposed contributions will enable the next generation of UxS with higher levels of autonomy to generate more effective Human-Swarm Teams (HSTs)
Experimental analysis and modelling of the behavioural interactions underlying the coordination of collective motion and the propagation of information in fish schools
Les bancs de poissons sont des entités pouvant regrouper plusieurs milliers d'individus qui se déplacent de façon
synchronisée, dans un environnement sujet à de multiples perturbations, qu'elles soient endogènes (e.g. le départ soudain
d'un congénère) ou exogènes (e.g. l'attaque d'un prédateur). La coordination de ces bancs de poissons, décentralisée, n'est
pas encore totalement comprise. Si les mécanismes sous-jacents aux interactions sociales proposés dans des travaux
précédents reproduisent qualitativement les structures collectives observées dans la nature, la quantification de ces
interactions et l'accord quantitatif entre ces mesures individuelles et les motifs collectifs sont encore rares dans les
recherches récentes et forment l'objet principal de cette thèse.
L'approche de ce travail repose sur une étroite combinaison entre les méthodes expérimentales et de modélisation dans
l'objectif de découvrir les liens entre les comportements individuels et les structures observées à l'échelle collective.
Nous avons caractérisé et quantifié les interactions et mécanismes à l'origine, d'abord, de la coordination des individus
dans les bancs de poissons et, ensuite, de la propagation d'information, quand le groupe subit une perturbation endogène ou
exogène. Ces travaux, tous réalisés en étudiant la même espèce de poisson d'eau douce, le nez-rouge (Hemigrammus
rhodostomus), ont mobilisĂ© une diversitĂ© de mĂ©thodes expĂ©rimentales, d'analyses statistique et de modĂ©lisation, Ă
l'interface de l'Ă©thologie, de la physique statistique et des sciences computationnelles.Fish schools are systems in which thousands of individuals can move in a synchronised manner in a changing environment, with
endogenous perturbations (e.g. when a congener leaves the group) or exogenous (e.g. the attack of a predator). The
coordination of fish schools, decentralised, is not completely understood yet. If the mechanisms underlying social
interactions discussed in previous studies qualitatively match the social patterns observed in nature, the quantification of
these interactions and the quantitative match between individual measurements and collective patterns are still sparse in
recent works and are the main focus of this thesis.
This work combines closely experimental and modelling methods in order to investigate the links between the individual
behaviours and the patterns observed at the collective scale. We have characterised and quantified the interactions and
mechanisms at the origin of, first, the coordination of individuals in fish schools and, second, the propagation of
information, when the group is under endogenous or exogenous perturbations. This thesis focuses on one freshwater fish
species, the rummy-nose tetra (Hemigrammus rhodostomus), and is the result of a diversity of experimental methods,
statistical analyses and modelling, at the interface of ethology, statistical physics and computational sciences
An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots
The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so
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