Soft Control of Self-organized Locally Interacting Brownian Planar Agents

This contribution is addressed to the dynamics of heterogeneous interacting agents evolving on the plane. Heterogeneity is due to the presence of an unfiltered externally controllable fellow, a shill, which via mutual interactions ultimately drives (i.e. soft controls) the whole society towards a given goal. We are able to calculate relevant dynamic characteristics of this controllable agent. This opens the possibility to optimize the soft controlling of a whole society by infiltrating it with a properly designed shill. Numerical results fully corroborate our theoretical findings

Spontaneous membrane formation and self-encapsulation of active rods in an inhomogeneous motility field

We study the collective dynamics of self-propelled rods in an inhomogeneous motility field. At the interface between two regions of constant but different motility, a smectic rod layer is spontaneously created through aligning interactions between the active rods, reminiscent of an artificial, semi-permeable membrane. This "active membrane" engulfes rods which are locally trapped in low-motility regions and thereby further enhances the trapping efficiency by self-organization, an effect which we call "self-encapsulation". Our results are gained by computer simulations of self-propelled rod models confined on a two-dimensional planar or spherical surface with a stepwise constant motility field, but the phenomenon should be observable in any geometry with sufficiently large spatial inhomogeneity. We also discuss possibilities to verify our predictions of active-membrane formation in experiments of self-propelled colloidal rods and vibrated granular matter

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

A multi-layer model for self-propelled disks interacting through alignment and volume exclusion

We present an individual-based model describing disk-like self-propelled particles moving inside parallel planes. The disk directions of motion follow alignment rules inside each layer. Additionally, the disks are subject to interactions with those of the neighboring layers arising from volume exclusion constraints. These interactions affect the disk inclinations with respect to the plane of motion. We formally de-rive a macroscopic model composed of planar Self-Organized Hydrodynamic (SOH) models describing the transport of mass and evolution of mean direction of motion of the disks in each plane, supplemented with transport equations for the mean disk inclination. These planar models are coupled due to the interactions with the neighboring planes. Numerical comparisons between the individual-based and macroscopic models are carried out. These models could be applicable, for instance, to describe sperm-cell collective dynamics

Emergent behavior in active colloids

Active colloids are microscopic particles, which self-propel through viscous fluids by converting energy extracted from their environment into directed motion. We first explain how articial microswimmers move forward by generating near-surface flow fields via self-phoresis or the self-induced Marangoni effect. We then discuss generic features of the dynamics of single active colloids in bulk and in confinement, as well as in the presence of gravity, field gradients, and fluid flow. In the third part, we review the emergent collective behavior of active colloidal suspensions focussing on their structural and dynamic properties. After summarizing experimental observations, we give an overview on the progress in modeling collectively moving active colloids. While active Brownian particles are heavily used to study collective dynamics on large scales, more advanced methods are necessary to explore the importance of hydrodynamic and phoretic particle interactions. Finally, the relevant physical approaches to quantify the emergent collective behavior are presented.Comment: 31 pages, 14 figure

Intearcting Brownian Swarms: Some Analytical Results

We consider the dynamics of swarms of scalar Brownian agents subject to local imitation mechanisms implemented using mutual rank-based interactions. For appropriate values of the underlying control parameters, the swarm propagates tightly and the distances separating successive agents are iid exponential random variables. Implicitly, the implementation of rank-based mutual interactions, requires that agents have infinite interaction ranges. Using the probabilistic size of the swarm’s support, we analytically estimate the critical interaction range below that flocked swarms cannot survive. In the second part of the paper, we consider the interactions between two flocked swarms of Brownian agents with finite interaction ranges. Both swarms travel with different barycentric velocities, and agents from both swarms indifferently interact with each other. For appropriate initial configurations, both swarms eventually collide (i.e., all agents interact). Depending on the values of the control parameters, one of the following patterns emerges after collision: (i) Both swarms remain essentially flocked, or (ii) the swarms become ultimately quasi-free and recover their nominal barycentric speeds. We derive a set of analytical flocking conditions based on the generalized rank-based Brownian motion. An extensive set of numerical simulations corroborates our analytical findings

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Programmable interactions with biomimetic DNA linkers at fluid membranes and interfaces

At the heart of the structured architecture and complex dynamics of biological systems are specific and timely interactions operated by biomolecules. In many instances, biomolecular agents are spatially confined to flexible lipid membranes where, among other functions, they control cell adhesion, motility and tissue formation. Besides being central to several biological processes, \emph{multivalent interactions} mediated by reactive linkers confined to deformable substrates underpin the design of synthetic-biological platforms and advanced biomimetic materials. Here we review recent advances on the experimental study and theoretical modelling of a heterogeneous class of biomimetic systems in which synthetic linkers mediate multivalent interactions between fluid and deformable colloidal units, including lipid vesicles and emulsion droplets. Linkers are often prepared from synthetic DNA nanostructures, enabling full programmability of the thermodynamic and kinetic properties of their mutual interactions. The coupling of the statistical effects of multivalent interactions with substrate fluidity and deformability gives rise to a rich emerging phenomenology that, in the context of self-assembled soft materials, has been shown to produce exotic phase behaviour, stimuli-responsiveness, and kinetic programmability of the self-assembly process. Applications to (synthetic) biology will also be reviewed.Comment: 63 pages, revie

Cytoskeleton and Cell Motility

The present article is an invited contribution to the Encyclopedia of Complexity and System Science, Robert A. Meyers Ed., Springer New York (2009). It is a review of the biophysical mechanisms that underly cell motility. It mainly focuses on the eukaryotic cytoskeleton and cell-motility mechanisms. Bacterial motility as well as the composition of the prokaryotic cytoskeleton is only briefly mentioned. The article is organized as follows. In Section III, I first present an overview of the diversity of cellular motility mechanisms, which might at first glance be categorized into two different types of behaviors, namely "swimming" and "crawling". Intracellular transport, mitosis - or cell division - as well as other extensions of cell motility that rely on the same essential machinery are briefly sketched. In Section IV, I introduce the molecular machinery that underlies cell motility - the cytoskeleton - as well as its interactions with the external environment of the cell and its main regulatory pathways. Sections IV D to IV F are more detailed in their biochemical presentations; readers primarily interested in the theoretical modeling of cell motility might want to skip these sections in a first reading. I then describe the motility mechanisms that rely essentially on polymerization-depolymerization dynamics of cytoskeleton filaments in Section V, and the ones that rely essentially on the activity of motor proteins in Section VI. Finally, Section VII is devoted to the description of the integrated approaches that have been developed recently to try to understand the cooperative phenomena that underly self-organization of the cell cytoskeleton as a whole.Comment: 31 pages, 16 figures, 295 reference