Effects of anisotropic interactions on the structure of animal groups

This paper proposes an agent-based model which reproduces different structures of animal groups. The shape and structure of the group is the effect of simple interaction rules among individuals: each animal deploys itself depending on the position of a limited number of close group mates. The proposed model is shown to produce clustered formations, as well as lines and V-like formations. The key factors which trigger the onset of different patterns are argued to be the relative strength of attraction and repulsion forces and, most important, the anisotropy in their application.Comment: 22 pages, 9 figures. Submitted. v1-v4: revised presentation; extended simulations; included technical results. v5: added a few clarification

Kinetic description of swarming dynamics with topological interaction and emergent leaders

In this paper, we present a model describing the collective motion of birds. We explore the dynamic relationship between followers and leaders, wherein a select few agents, known as leaders, can initiate spontaneous changes in direction without being influenced by external factors like predators. Starting at the microscopic level, we develop a kinetic model that characterizes the behaviour of large crowds with transient leadership. One significant challenge lies in managing topological interactions, as identifying nearest neighbors in extensive systems can be computationally expensive. To address this, we propose a novel stochastic particle method to simulate the mesoscopic dynamics and reduce the computational cost of identifying closer agents from quadratic to logarithmic complexity using a $k$-nearest neighbours search algorithm with a binary tree. Lastly, we conduct various numerical experiments for different scenarios to validate the algorithm's effectiveness and investigate collective dynamics in both two and three dimensions

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

Control of agent swarms in random environments

The collective dynamic behavior of large groups of interacting autonomous agents (swarms) have inspired much research in both fundamental and engineering sciences. It is now widely acknowledged that the intrinsic nonlinearities due to mutual interactions can generate highly collective spatio-temporal patterns. Moreover, the resulting self-organized behavior cannot be simply guessed by solely investigating the elementary dynamic rules of single individuals. With a view to apply swarm collective behaviors to engineering, it is mandatory to thoroughly understand and master the mechanism of emergence to ultimately address the basic question: What individual dynamics and what type of interactions generate a given stable collective spatio-temporal behavior ? The present doctoral work is a contribution to the general common effort devoted to give an engineering operational answer to this simple and yet still highly challenging question. Swarms modeling is based on the dynamic properties of multi-agents systems (MAS). Methodological approaches for studying MAS are i) mathematics, ii) numerical simulation and iii) experimental validation on physical systems. While in this work we strive to construct and analytically solve new classes of mathematical MAS models, we also make a very special effort to develop new MAS modeling platforms for which one is simultaneously able to offer exact analytical results, corroborate these via simulation and finally implement the resulting control mechanism on swarms of actual robots. In full generality, MAS are formed by mutually interacting autonomous agents evolving in random environments. The presence of noise sources will indeed be unavoidable in any actual implementation. This drives us to consider coupled sets of stochastic nonlinear differential equations as being the natural mathematical modeling framework. We first focus on the simplest situations involving homogeneous swarms. Here, for large homogeneous swarms, the mean-field approach (borrowed from statistical physics) can be used to analytically characterize the resulting spatio-temporal patterns from the individual agent dynamics. In this context, we propose a new modeling platform (the so-called mixed-canonical dynamics) for which we are able to fully bridge the gap between pure mathematics and actual robotic implementation. In a second approach, we then consider heterogeneous swarms realized either when one agent behaves either as a leader or a shill (i.e as an infiltrated agent), or when two different sub-swarms compose the whole MAS. Analytical results are generally very hard to find for heterogeneous swarms, since the mean-field approach cannot be used. In this context, we use recent results in rank-based Brownian motions to approach some heterogeneous MAS models. In particular, we are able to analytically study i) a case of soft control of the swarm by a shill agent, and ii) the mutual interactions between two different societies (i.e., sub-groups) of homogeneous agents. Finally, the same mathematical framework enables us to consider a class of MAS where agents mutually interact via their environment (stigmergic interactions). Here, we can once again simultaneously present analytical results, numerical simulations and to ultimately implement the controller on a swarm of robotic boats

Swarm robotic systems: ypod formation with analysis on scalability and stability

Aquesta tesi se central en la formació d’eixams, on s’estudia el comportament coordinat d’un grup de robots per formar un patró quan s’observa a nivell global. En aquest sentit, la formació de la forma general és un dels problemes actuals en d’intel·ligència d’eixams artificials. En aquesta tesi s’introdueix una nova formació en forma de Y, la qual presenta una gran quantitat d’aplicacions en comparació amb altres tècniques de formació. Per exemple, la formació en Y es pot aplicar com a formació estratègica per totes les escales, presenta facilitat per canviar de forma i grandària a més de resoldre els problemes de redundància, d’autoorganització i autoreparació. L’objectiu principal d’aquesta tesi és aconseguir la formació en Y d’un eixam de robots. La implementació de dita formació únicament s’ha dut a terme mitjançant un entorn de simulació tot i que se han tingut en compte diferents aspectes que es podrien donar en una implementació real. El disseny del control de l’eixam per a diferents eixos s’ha realitzat a partir d’un model capaç de predir el comportament global de l’eixam, de la definició del temps d’establiment i l’aplicació de tècniques de localització de pols. Per controlar l’eixam en forma Y en termes d’orientació i el seu moviment com un bloc, s’han combinat el controlador lineal proposat, amb funcions límit i l’ajust d’alguns paràmetres per simulació. Els paràmetres s’han escollit per la formació desitjada i segons les constants definides per l’usuari. En comparació amb altres treballs, la solució proposta és simple, computacionalment eficient i tant per models d’eixams centralitzats com descentralitzats.Esta tesis se centra en la formación de enjambres, donde se estudia el comportamiento coordinado de un grupo de robots para formar un patrón cuando se observa a nivel global. En este sentido, la formación de la forma general es uno de los problemas actuales en la inteligencia de enjambres artificiales. En esta tesis se introduce una nueva formación en forma de Y, la cual presenta una gran cantidad de aplicaciones en comparación con otras técnicas de formación. Por ejemplo, la formación en Y se puede aplicar como formación estratégica para todas las escalas, presenta facilidad para cambiar de forma y tamaño además de resolver los problemas de redundancia, de auto-organización y auto-reparación. El objetivo principal de esta tesis es conseguir la formación en Y de un enjambre de robots. La implementación de dicha formación se ha llevado a cabo únicamente mediante un entorno de simulación aunque se han tenido en cuenta diferentes aspectos que se podrían dar en una implementación real. El diseño del control del enjambre para diferentes ejes se ha realizado a partir de un modelo capaz de predecir el comportamiento global del enjambre, de la definición del tiempo de establecimiento y la aplicación de técnicas de localización de polos. Para controlar el enjambre en forma de Y en términos de orientación y movimientos del enjambre como un bloque, se han combinado el controlador lineal propuesto, funciones límite y el ajuste de algunos parámetros por simulación. Los parámetros se han escogido para la formación deseada y según las constantes definidas por el usuario. En comparación con otros trabajos, la solución propuesta es simple, computacionalmente eficiente, y tanto para modelos de enjambres centralizados como descentralizados.The context of this work is the innovative young filed of swarm robotics. Particularly, in this thesis focused on swarm formation, which is important in swarm robotics too since coordinated behaviour of a group of robots to form a pattern when viewed globally. In this regard, global shape formation is one of the ongoing problems in artificial swarm intelligence. In nature, it is performed for various purposes, and search and rescue swarms could be used in disaster areas .In robotics phenomena, there exist various shape formations in the literature, but in this thesis, introduced new shape formation Y-Pod, which has vast applications compare to other formation techniques. In the discussion of our research journey, me and my supervisor discussed about various shape formations but finally exploit new shape formation Y-Pod and when we think about it, arise some questions ,why Y-Pod swarm formation and what it will serve, so in our casual discussion some important advantages are identified, those are : The Y-Pod can be utilized for formation strategy on all scales, Global shape formation, when viewed globally, Changes shapes, Easy to expand, overcome the redundancy problems and Self-organized and self-repair problems. The main objective of the proposed approach is to form a Y-pod formation of swarm robots. As well as we keep in our mind for real robot performance task, but the original work is delivered in simulation based environment only. Several parameters that significantly define the resulting behavior. We have proposed system equilibrium parameters with settling time and pole based problems, to control the swarm system in various axis an accurate model will predict the global behavior of the Y-Pod swarm formation based on the mathematical identified parameters. The proposed linear controller, limiting functions and simulation tuned parameters are combined to control Y-Pod swarm formation in terms of orientation, and swarm movement as a whole. Parameters are chosen based on desired formation as well as user defined constraints. This approach compared to others, is simple, computationally efficient, scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models

SWARM INTELLIGENCE AND STIGMERGY: ROBOTIC IMPLEMENTATION OF FORAGING BEHAVIOR

Swarm intelligence in multi-robot systems has become an important area of research within collective robotics. Researchers have gained inspiration from biological systems and proposed a variety of industrial, commercial, and military robotics applications. In order to bridge the gap between theory and application, a strong focus is required on robotic implementation of swarm intelligence. To date, theoretical research and computer simulations in the field have dominated, with few successful demonstrations of swarm-intelligent robotic systems. In this thesis, a study of intelligent foraging behavior via indirect communication between simple individual agents is presented. Models of foraging are reviewed and analyzed with respect to the system dynamics and dependence on important parameters. Computer simulations are also conducted to gain an understanding of foraging behavior in systems with large populations. Finally, a novel robotic implementation is presented. The experiment successfully demonstrates cooperative group foraging behavior without direct communication. Trail-laying and trail-following are employed to produce the required stigmergic cooperation. Real robots are shown to achieve increased task efficiency, as a group, resulting from indirect interactions. Experimental results also confirm that trail-based group foraging systems can adapt to dynamic environments