Fluid-Mediated Stochastic Self-Assembly at Centimetric and Sub-Millimetric Scales: Design, Modeling, and Control

Stochastic self-assembly provides promising means for building micro-/nano-structures with a variety of properties and functionalities. Numerous studies have been conducted on the control and modeling of the process in engineered self-assembling systems constituted of modules with varied capabilities ranging from completely reactive nano-/micro-particles to intelligent miniaturized robots. Depending on the capabilities of the constituting modules, different approaches have been utilized for controlling and modeling these systems. In the quest of a unifying control and modeling framework and within the broader perspective of investigating how stochastic control strategies can be adapted from the centimeter-scale down to the (sub-)millimeter-scale, as well as from mechatronic to MEMS-based technology, this work presents the outcomes of our research on self-assembly during the past few years. As the first step, we leverage an experimental platform to study self-assembly of water-floating passive modules at the centimeter scale. A dedicated computational framework is developed for real-time tracking, modeling and control of the formation of specific structures. Using a similar approach, we then demonstrate controlled self-assembly of microparticles into clusters of a preset dimension in a microfluidic chamber, where the control loop is closed again through real-time tracking customized for a much faster system dynamics. Finally, with the aim of distributing the intelligence and realizing programmable self-assembly, we present a novel experimental system for fluid-mediated programmable stochastic self-assembly of active modules at the centimeter scale. The system is built around the water-floating 3-cm-sized Lily robots specifically designed to be operative in large swarms and allows for exploring the whole range of fully-centralized to fully-distributed control strategies. The outcomes of our research efforts extend the state-of-the-art methodologies for designing, modeling and controlling massively-distributed, stochastic self-assembling systems at different length scales, constituted of modules from centimetric down to sub-millimetric size. As a result, our work provides a solid milestone in structure formation through controlled self-assembly.

Design, Modeling and Optimization of Stochastic Reactive Distributed Robotic Systems

Grégory Mermoud

This dissertation describes a complete methodological framework for designing, modeling and optimizing a specific class of distributed systems whose dynamics result from the multiple, stochastic interactions of their constitutive components. These components can be robots endowed with very minimal capabilities, or even simpler entities such as insects, bacteria, particles, or molecules. We refer to such components as Smart Minimal Particles (SMPs). One of the main difficulties facing the modeling of SMPs is the potential complexity and richness of their dynamics. On the one hand, one needs detailed models that account for the physico-chemical properties of the lower-level components (e.g., shape, material, surface chemistry, charge, etc.), which, in turn, determine the nature and the magnitude of their interactions. On the other hand, one is also interested in models that can yield accurate numerical predictions of macroscopic quantities, and investigate formally their dependence on the system’s design and control parameters. These competing requirements motivate a combination of models at multiple levels of abstraction, as advocated by the Multi-Level Modeling Methodology (MLMM), which was introduced in prior works. The MLMM enables the fulfillment of both requirements in a very efficient way by incrementally building up models at increasing levels of abstraction in order to capture the relevant features of the system. This thesis extends and consolidates the MLMM along several axes. In a first step, we provide a theoretical consolidation of the MLMM. We propose a thorough classification of the different models of SMPs, and we discuss their underlying assumptions and simplifications. We shed light on the fundamental impact of embodiment and spatiality on models’ accuracy, and we define the conditions under which the macro-deterministic approximation is valid. These theoretical considerations are experimentally supported by five case studies of aggregation and Self-Assembly (SA) at different scales. The five case studies utilize three types of components: (i) miniature wheeled robots (Alice, 2 cm in size) endowed with limited computation, sensing, actuation, and communication capabilities, (ii) water-floating passive modules (Lily, 3 cm in size) endowed with four permanent magnets for mutual latching, and (iii) micro-fabricated cylinders (about 100 μm in diameter, studied in realistic simulation only) that can achieve SA in liquids. In a second step, we introduce the core contribution of this thesis, that is, a systematic and generic methodology for bridging the gap between real, physical systems and computationally efficient models at multiple abstraction levels. In particular, we describe the M3 computational framework, which enables the automated construction of models of SMPs. Our approach consists in observing (or simulating realistically) a system of interest, and building a hierarchical suite of models based on the observations (i.e., trajectories) collected during these experiments (or simulations). Internally, the framework first builds up a microscopic representation of the system based on these observations and on a list of interactions of interest specified by the user. This representation, called the Canonical Microscopic Model (CMM), is a formal and generic description of SMPs, and it serves as a blueprint for the construction of a macroscopic model, specified using the Chemical Reaction Network (CRN) formalism. The rates of the CRN are finally calibrated using a Maximum Likelihood Estimation (MLE) scheme. We validate the M3 framework on each of the three platforms discussed earlier, thereby illustrating its relevance both as a modeling and as an analysis tool. Finally, we discuss the role of multi-level modeling when designing and optimizing SMPs. In particular, we show that top-down model-based design of multi-robot systems is generally not amenable to efficient implementations when dealing with very resource-constrained robots. Instead, faithful and computationally efficient models built incrementally from the bottom up prove to be an essential tool for designing such systems. We further corroborate this claim by applying our automated modeling framework to the real-time control of the stochastic SA of Lily modules. Our scientific contribution is therefore three-fold. First, we provide a solid experimental and theoretical consolidation of the MLMM, which has been the subject of intense research efforts for the last decade. Second, we propose, for the first time, an approach to generate models at high abstraction level in a completely automated fashion, based solely on observations of the system of interest. Third, we provide deep insights into the modeling and the design of SMPs, with a specific emphasis on self-assembling systems ranging from the centimeter scale down to the micrometer scale.

EPFL2012

Lightweight physics-based models for the control of fluid-mediated self-assembly of robotic modules

Bahar Haghighat, Hala Khodr, Alcherio Martinoli

Self-assembling robotic systems form a subclass of distributed robotic systems that undertake the fundamental task of structure formation. These systems build desired target structures by putting their constituting robotic modules together in a distributed and stochastic fashion, i.e., through a self-assembly process. The use of self-assembly as the underpinning coordination mechanism provides powerful means for structure formation across a variety of length scales as well as media. In particular, fluidic media have been shown to be very efficient enablers for small-scale self-assembly. In this paper, we consider a distributed robotic system consisting of multiple miniature robotic modules performing self-assembly in 2D, at the water-air interface. The course of the assembly process in the system culminating in a predefined target structure is shaped by the ruleset controllers programmed on the individual robotic modules, allowing only certain formations and ruling out others throughout the process. Designing control strategies relies heavily on accurate models of the system dynamics. Faithfully modeling such systems and their inter-module interactions involves capturing the hydrodynamic forces acting on the modules using typically computationally expensive fluid dynamic modeling tools. Such computational cost restricts the usability of the resulting models, particularly for the purpose of designing optimized controllers. In this paper, we present a new modeling approach and proceed by employing the resulting model for optimizing ruleset controllers. First, we show how the hardware and firmware of the robotic platform can be faithfully modeled in a high-fidelity robotic simulator. Second, we develop a physics plugin to recreate the hydrodynamic forces acting on the modules and propose a trajectory-based method for calibrating the plugin model parameters. Finally, we employ the resulting model and obtain automatically optimized ruleset controllers for given target structures. (C) 2019 Published by Elsevier B.V.

ELSEVIER2019

Real-Time Automated Modeling and Control of Self-Assembling Systems

Alcherio Martinoli, Massimo Mastrangeli, Grégory Mermoud, Utkarsh Upadhyay

We present the M3 framework, a formal and generic computational framework for modeling and controlling distributed stochastic systems of purely reactive robots in an automated and real-time fashion. Based on the trajectories of the robots, the framework builds up an internal microscopic representation of the system, which then serves as a blueprint of models at higher abstraction levels. These models are then calibrated using a Maximum Likelihood Estimation (MLE) approach. We illustrate the structure and performance of the framework by performing the online optimization of a simple bang-bang controller for the stochastic self-assembly of water- floating passive robots. The experimental results demonstrate that the generated models can successfully optimize the assem- bly of desired structures.