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Design, Modeling, and Control Methodologies for Self-Assembling Floating Miniature Robotic Systems

English title Design, Modeling, and Control Methodologies for Self-Assembling Floating Miniature Robotic Systems
Applicant Martinoli Alcherio
Number 137838
Funding scheme Project funding (Div. I-III)
Research institution Laboratoire de systèmes et algorithmes intelligents distribués EPFL - ENAC - IIE - DISAL
Institution of higher education EPF Lausanne - EPFL
Main discipline Other disciplines of Engineering Sciences
Start/End 01.11.2012 - 31.10.2014
Approved amount 149'002.00
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All Disciplines (3)

Discipline
Other disciplines of Engineering Sciences
Information Technology
Electrical Engineering

Keywords (5)

Self-assembly; Swarm robotics; Distributed stochastic control; Multi-level modeling; Miniature embedded systems

Lay Summary (French)

Lead
Au cours des dernières années, les avancées technologiques ont orientée la communauté robotique vers le domaine de la haute miniaturisation. Les robots ultra-petits peuvent accéder à des environnements qui sont hors de la portée des plates-formes robotiques typiques. Cependant, la miniaturisation est au prix d’une capacité computationnel, sensoriel, d’actuation et de communication minimaliste. Ces restrictions sévères créent la nécessité d'une solution collaborative des tâches en misant sur les comportements collectifs auto-organisés.
Lay summary

Au cœur du projet se trouvent les concepts d'agrégation et d’auto-assemblage. Les interactions physiques ou à faible portée sont les options les plus plausibles pour réaliser une certaine forme de partage d'information collaboratif qui a le potentiel d'être miniaturisé pour des appareils encore plus petits; l'agrégation physique est la condition nécessaire pour obtenir une réponse collective coordonnée du système. Une première catégorie d'expériences sera concernée par la formation distribuée de modèles commandés (par exemple, auto-assemblage). Une seconde catégorie plus complexe d'expériences mettra l'accent sur la formation de configurations fonctionnelles et de structures adaptatives autour de modèles statiques ou dynamiques spécifiques agissant comme précurseurs de tâches futures  impliquant  la protection, le transport ou la destruction d’un objet.

Dans cette première phase du projet, l’objectif est de développer une plate-forme expérimentale robotique distribuée pour l’étude de l’auto-assemblage dans un milieu fluide; par la suite on vise à insérer la plate-forme dont les ressources sont limitées dans un seul cadre de modélisation multi-niveaux et d'explorer de façon distribué, stochastique et originale les approches de contrôle.

Direct link to Lay Summary Last update: 29.10.2014

Responsible applicant and co-applicants

Employees

Publications

Publication
Characterization and Validation of a Novel Robotic System for Fluid-Mediated Programmable Stochastic Self-Assem
Haghighat Bahar, Martinoli Alcherio (2016), Characterization and Validation of a Novel Robotic System for Fluid-Mediated Programmable Stochastic Self-Assem, in IEEE/RSJ International Conference on Intelligent Robots and Systems, DaejeonIEEE, Daejeon.
Synthesizing Rulesets for Programmable Robotic Self-Assembly: A Case Study using Floating Miniaturized Robots
Haghighat Bahar, Platterrier Brice, Waegeli Loic, Martinoli Alcherio (2016), Synthesizing Rulesets for Programmable Robotic Self-Assembly: A Case Study using Floating Miniaturized Robots, in International Conference on Swarm Intelligence, BruxellesSpringer International Publishing, Switzerland.
Fluid-Mediated Stochastic Self-Assembly at Centimetric and Sub-Millimetric Scales: Design, Modeling, and Control
Haghighat Bahar, Mastrangeli Massimo, Mermoud Grégory, Schill Felix, Martinoli Alcherio (2016), Fluid-Mediated Stochastic Self-Assembly at Centimetric and Sub-Millimetric Scales: Design, Modeling, and Control, in Micromachines, 7(8), 138.
Lily: A Miniature Floating Robotic Platform for Programmable Stochastic Self-Assembly
Haghighat Bahar, Droz Emmanuel, Martinoli Alcherio (2015), Lily: A Miniature Floating Robotic Platform for Programmable Stochastic Self-Assembly, in IEEE International Conference on Robotics and Automation (ICRA), Seattle.
A Rule Synthesis Algorithm for Programmable Stochastic Self-Assembly of Robotic Modules
Haghighat Bahar, Martinoli Alcherio, A Rule Synthesis Algorithm for Programmable Stochastic Self-Assembly of Robotic Modules, in Kolling Andreas , Gauci Melvin, Frazzoli Emilio, Matsuno Fumitoshi , Roderich Gross, Berman Spring, Martinoli Alcherio (ed.), Springer International Publishing, Switzerland.
Automatic Synthesis of Rulesets for Programmable Stochastic Self- assembly of Rotationally Symmetric Robotic Modules
Haghighat Bahar, Martinoli Alcherio, Automatic Synthesis of Rulesets for Programmable Stochastic Self- assembly of Rotationally Symmetric Robotic Modules, in Swarm Intelligence.
Probabilistic Modeling of Programmable Stochastic Self-Assembly of Robotic Modules
Haghighat Bahar, Thandiackal Robin, Mordig Maximilian, Martinoli Alcherio, Probabilistic Modeling of Programmable Stochastic Self-Assembly of Robotic Modules, in IEEE/RSJ International Conference on Intelligent Robots and Systems, VancouverIEEE, Vancouver.

Collaboration

Group / person Country
Types of collaboration
Radhika Nagpal United States of America (North America)
- in-depth/constructive exchanges on approaches, methods or results
- Exchange of personnel

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
13th International Symposium on Distributed Autonomous Robotic Systems (DARS) Talk given at a conference A Rule Synthesis Algorithm for Programmable Stochastic Self-Assembly of Robotic Modules 06.11.2016 London, Great Britain and Northern Ireland Haghighat Bahar;
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems Talk given at a conference Characterization and Validation of a Novel Robotic System for Fluid-Mediated Programmable Stochastic Self-Assembly 09.10.2016 Daejeon, Korean Republic (South Korea) Haghighat Bahar;
International Conference on Swarm Intelligence Talk given at a conference Synthesizing Rulesets for Programmable Robotic Self-Assembly: A Case Study using Floating Miniaturized Robots 07.09.2016 Bruxelles, Belgium Haghighat Bahar;
2015 IEEE International Conference on Robotics and Automation (ICRA) Talk given at a conference Lily: A Miniature Floating Robotic Platform for Programmable Stochastic Self-Assembly 26.05.2015 Seattle, United States of America Haghighat Bahar;


Associated projects

Number Title Start Funding scheme
116913 Design, Control, and Optimization of Swarm-Intelligent, Real-Time, Embedded Systems 01.08.2007 SNSF Professorships
149543 Distributed Mitigation of Wind-Induced Vibrations in Long-Span Bridges 01.02.2014 Project funding (Div. I-III)
157191 A Modeling and Control Methodological Framework for Self-Assembling Floating Miniature Robotic Systems 01.11.2014 Project funding (Div. I-III)
157191 A Modeling and Control Methodological Framework for Self-Assembling Floating Miniature Robotic Systems 01.11.2014 Project funding (Div. I-III)
113795 Trajectory Analysis and Behavioral Identification in Mobile Robotic Systems 01.01.2007 Project funding (Div. I-III)

Abstract

Technological breakthroughs in the field of micro- and nanoengineering are steering the robotics community towards the realm of high miniaturization. Very-small robots (e.g., robots smaller than one millimeter in size) could access environments that are beyond the reach of typical robotic platforms, and thus hold great promises in a large variety of disciplines, including biomedical engineering, pervasive information technology, and environmental engineering. However, miniaturization comes at a price: minimalist computation, sensing, actuation, and communication capabilities. These severe restrictions create the need for a collaborative approach towards the solution of non-trivial tasks by leveraging self-organized collective behaviors. A large body of research in swarm robotics has been dedicated to the study and the design of distributed, stochastic control strategies that are scalable both in terms of swarm size (i.e., they can be implemented in swarms of thousands of robots or more) and individual node size (i.e., they can be implemented using miniature robots with very limited capabilities). Within the broader perspective of investigating how one can transpose and adapt such control strategies from the centimeter-scale down to the (sub-)millimeter-scale, and from mechatronics technology to MEMS technology, the present project intends to develop a flexible experimental platform (called Lily) built around centimeter-sized intelligent devices designed through standard mechatronic technology, whose primary goal is to serve as a physical test bed for distributed, stochastic control strategies and corresponding modeling methods. Working at the centimeter scale, where the core of the robotic expertise of our laboratory has been built in recent years, readily allows for the development of new experimental tools (e.g., robotic nodes, monitoring and agitation systems) and methods (e.g., modeling, control, optimization) which, while of considerable importance per se, will represent a solid milestone for future downscaling and adaptation to platforms of much smaller dimensions.At the heart of the project lie the concepts of aggregation and self-assembly. We believe that, regardless of the tremendous promises of MEMS technology, actual long-range communication at sub-millimetric scales will not be feasible before years. Thus, relatively short-range interactions or physical contact will meanwhile remain the most viable options to achieve some form of collaborative information sharing which has the potential to be downscaled to even smaller devices; and physical aggregation is the necessary condition for achieving a collectively coordinated response of the system. A first class of experiments will be concerned with the distributed formation of ordered patterns (i.e., self-assembly). A second, slightly more complex class of experiments will focus on the formation of functional configurations and adaptive structures around specific static or dynamic templates serving as precursors of future, more involved tasks such as object protection, transport, or destruction.The project is basically divided into two main research thrusts: (i) a technological research thrust concerned with the design, the fabrication, and the packaging of the experimental platform, and (ii) a theoretical research thrust concerned with the development of distributed, stochastic control schemes supported by a flexible modeling framework. Such a modeling framework shall enable to predict and optimize the dynamics of the proposed distributed robotic platform as a function of a large variety of design and control parameters, therefore strongly linking the two research thrusts. The design of such modeling framework will be done in a modular way so that specific features of future sub-millimeter platforms will be easy to incorporate thereby. The expected outcomes of this project are two-fold. First, our research will consolidate and improve the design of centimeter-sized distributed robotic platforms based on mechatronic technology. Second, we aim at updating and extending state-of-the-art methodologies for designing, modeling, and controlling massively distributed, stochastic robotic systems. This project is definitely oriented towards basic research: even though it aims at delivering concrete demonstrators, it does not yet target any specific application. In summary, this project aims to: (i) develop a flexible distributed robotic platform serving as physical, centimeter-scale emulator of micro-devices; (ii) capture the resource-constrained platform within a single multi-level modelling framework, allowing for in-depth formal analysis and synthesis of the properties of the self-assembling robotic system; (iii) explore original distributed, stochastic control approaches potentially down-scalable to much smaller dimensions; (iv) increase the level of intelligence of the distributed robotic system as much as possible within the a priori established resource boundaries, by working both on the capabilities of the individual nodes and their local interaction rules.
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