Modeling and Optimization Methods for Self-Assembly Across Scales

The future of integrated systems lies in their ability to be reactive, adaptive, ubiquitous and reliable. Regardless their field of application, in order to be successful, they will need to carry out their task in an efficient and safe manner, without polluting or disturbing the target environment, which may be a river or the human body. As research advances, it appears more and more clearly that the future integrated systems fulfilling all these requirements will need to be both extremely miniaturized and massively distributed. Indeed, the intrinsic limitations of ultra-small integrated systems in term of sensing, actuation, and control prevent them to carry out a task individually. More specifically, distributed micro-robotic systems (i.e. individual robots smaller than one millimeter in size) hold great promises in a large variety of disciplines such as biomedical engineering, pervasive and ambient information technology, environmental engineering, and space exploration. However, there are at least two main obstacles lying on the route to functional swarms of micro-robots: (i) our inability to manufacture and package hybrid MEMS devices endowed with the three essential features of a robotic node, i.e. sensing, actuation, and control, and (ii) the lack of suitable control strategies and modeling methodologies for distributed robotic systems at this size range.

From this perspective, one need suitable methodologies for designing, fabricating, and controlling distributed intelligent systems in scalable fashion. Here, the concept of scalability is two-fold; a system can be scalable either in terms of size of the individual nodes (extremely miniaturized) or in terms of size of the swarm (massively distributed). From a control point of view, there is no evidence whatsoever that even cutting-edge distributed control strategies are scalable in either term since there is no experimental platform available for validating these strategies. Our research is primarily concerned with the development and the validation of actually scalable methodologies, with a strong emphasis on the model-based design and control of distributed intelligent systems, in close collaboration with another PhD student, supervised by Juergen Brugger, who will be responsible for the fabrication of these systems using a full breadth of innovative micromachining techniques.

At the heart of this research lies the concept of Self-Assembly (SA), i.e. the autonomous organization of components into an ordered structure. First, SA is envisioned as the key mechanism for packaging functional MEMS building blocks into hybrid functional micro-systems. Second, micro-robots are likely to suffer from a lack of reliability, efficiency, and robustness as individuals, but we expect them to team up for achieving a given task, notably by self-assembling into a superstructure of higher order, complexity, and functionality. As a result, we investigate SA at two different levels: (i) as a manufacturing and packaging method at individual device level and (ii) as a distributed control method for swarms of functional devices.

This project has been initiated as a collaborative effort within the EPFL Center for Integrated Systems. More information here.

Team and Collaborators

In collaboration with:

  • Jürgen Brugger
  • Loic Jacot-Descombes
  • Maurizio Gullo
  • Jeroen Steen
  • Vahid Fakhfouri




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

B. Haghighat; M. Mastrangeli; G. Mermoud; F. S. Schill; A. Martinoli 

Micromachines. 2016. Vol. 7, num. 8 (Special Issue on “Building by Self-Assembly”), p. 138. DOI : 10.3390/mi7080138.


Top-Down vs Bottom-Up Model-Based Methodologies for Distributed Control: A Comparative Experimental Study

G. Mermoud; U. Upadhyay; W. C. Evans; A. Martinoli 

2014. 12th International Symposium on Experimental Robotics, New Delhi, India, December 17-21, 2010. p. 615-629. DOI : 10.1007/978-3-642-28572-1_42.


Acousto-fluidic system assisting in-liquid self-assembly of microcomponents

J. Goldowsky; M. Mastrangeli; L. Jacot-Descombes; R. M. Gullo; G. Mermoud et al. 

Journal of Micromechanics and Microengineering. 2013. Vol. 23, num. 12, p. 125026. DOI : 10.1088/0960-1317/23/12/125026.

Fluid-mediated parallel self-assembly of polymeric micro-capsules for liquid encapsulation and release

L. Jacot-Descombes; C. Martin-Olmos; M. R. Gullo; V. J. Cadarso; G. Mermoud et al. 

Soft Matter. 2013. Vol. 9, p. 9931-9938. DOI : 10.1039/c3sm51923f.

Evaluating Efficient Data Collection Algorithms for Environmental Sensor Networks

W. C. Evans; A. Bahr; A. Martinoli 

2013. 10th International Symposium on Distributed Autonomous Robotic Systems, Lausanne, Switzerland, November 1-3, 2010. p. 77-89. DOI : 10.1007/978-3-642-32723-0_6.

Beat-Based Synchronization and Steering for Groups of Fixed-Wing Flying Robots

S. Hauert; S. Leven; J-C. Zufferey; D. Floreano 

2013. 10th International Symposium on Distributed Autonomous Robotic Systems. p. 281-293. DOI : 10.1007/978-3-642-32723-0_21.

A Plume Tracking Algorithm Based on Crosswind Formations

T. Lochmatter; E. A. Goel; I. Navarro; A. Martinoli 

2013. Distributed Autonomous Robotic Systems, Lausanne, Switzerland, November 2010. p. 91-102. DOI : 10.1007/978-3-642-32723-0_7.

Energy-Time Efficiency in Aerial Swarm Deployment

T. Stirling; D. Floreano 

2013. 10th International Symposium on Distributed Autonomous Robotic Systems, EPFL, Lausanne, Switzerland, November 1-3, 2010. p. 5-18. DOI : 10.1007/978-3-642-32723-0_1.

Distributed Autonomous Robotic Systems


Berlin Heidelberg: Springer, 2013.

Self-Organized Robotic Systems: Large-Scale Experiments in Aggregation and Self-Assembly using Miniature Robots

G. Mermoud; A. Prorok; L. Matthey; C. M. Cianci; N. Correll et al. 

Handbook of Collective Robotics; Singapore: Pan Stanford Publishing, 2013. p. 229-259.


Design, Modeling and Optimization of Stochastic Reactive Distributed Robotic Systems

G. Mermoud / A. Martinoli; J. Brugger (Dir.)  

Lausanne, EPFL, 2012. 

Physical interactions in swarm robotics: the hand-bot case study

M. Bonani; P. Rétornaz; S. Magnenat; H. Bleuler; F. Mondada 

2012. 10th International Symposium on Distributed Autonomous Robotic Systems (DARS 2010), Lausanne, Switzerland, November 1-3,2010. p. 585-595. DOI : 10.1007/978-3-642-32723-0_42.

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

G. Mermoud; M. Mastrangeli; U. Upadhyay; A. Martinoli 

2012. 2012 IEEE International Conference on Robotics and Automation, St. Paul, Minnesota, USA, May 14-18, 2012. p. 4266-4273. DOI : 10.1109/ICRA.2012.6224888.


Containers assembled in fluid and corresponding production

J. Brugger; C. Martin-olmos; A. Martinoli; G. Mermoud 

US2012145572; WO2010122499; WO2010122499.


A Trajectory-based Calibration Method for Stochastic Motion Models

E. Di Mario; G. Mermoud; M. Mastrangeli; A. Martinoli 

2011. IEEE/RSJ International Conference on Intelligent Robots and Systems. p. 4341-4347. DOI : 10.1109/IROS.2011.6094940.

Modeling Self-Assembly Across Scales: The Unifying Perspective of Smart Minimal Particles

M. Mastrangeli; G. Mermoud; A. Martinoli 

Micromachines. 2011. Vol. 2, num. 2, p. 82-115. DOI : 10.3390/mi2020082.


Modeling Self-Assembly at All Scales

G. Mermoud; L. Jacot-Descombes; R. M. Gullo; J. Brugger; A. Martinoli Annual Plenary Meeting, Bern, Switzerland, April 29, 2010.

SPM Measurements of Hydrophobic Interactions

R. M. Gullo; G. Mermoud; J. Y. Kim; L. Jacot-Descombes; A. Martinoli et al. Annual Plenary Meeting, Bern, Switzerland, April 29, 2010.

Surface-Tension-Driven Self-Assembly of Filled Cylinders with Different Geometry

L. Jacot-Descombes; G. Mermoud; R. M. Gullo; A. Martinoli; J. Brugger Annual Plenary Meeting, Bern, Switzerland, April 29, 2010.

Comparing and modeling distributed control strategies for miniature self-assembling robots

W. C. Evans; G. Mermoud; A. Martinoli 

2010. 2010 IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, USA, May 3-7, 2010. p. 1438-1445. DOI : 10.1109/ROBOT.2010.5509666.

Aggregation-mediated Collective Perception and Action in a Group of Miniature Robots

G. Mermoud; L. Matthey; W. C. Evans; A. Martinoli 

2010. 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2010), Toronto, Canada, May 10-14, 2010. p. 599-606.


Fluidic-mediated self-assembly for hybrid functional micro/nanosystems (SELFSYS)

L. Jacot-Descombes; C. Martin Olmos; G. Mermoud; E. Le-Caignec; A. Martinoli et al. 

Atelier LEA à Arc-et-Senans, Arc-et-Senans, France, September 8-9, 2009.

Drop-On-Demand Inkjet Printing of SU-8 Polymer

V. Fakhfouri; G. Mermoud; J. Y. Kim; A. Martinoli; J. Brugger 

Micro and Nanosystems. 2009. Vol. 1, num. 1, p. 63-67. DOI : 10.2174/1876402910901010063.

Towards Multi-Level Modeling of Self-Assembling Intelligent Micro-Systems

G. Mermoud; J. Brugger; A. Martinoli 

2009. 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009), Budapest, 10-15 May, 2009. p. 89–93.


Towards Smart Substrates for Controlling Micrometric Droplet Motion

G. Mermoud; V. Fakhfouri; A. Martinoli; J. Brugger 

2007. Foundations of Nanoscience (FNANO07): Self-Assembled Architectures and Devices, Snowbird, UT, April 18-April 21, 2007.


Inkjet-Based Deposition of Micro/Nano-Objects on Functional Surfaces – Controlling and Exploiting Self-Assembly across Length-Scales

G. Mermoud; V. Fakhfouri; A. Martinoli; J. Brugger 

LEA Laboratoire Européen Associé en Microtechnique, France, 12-13 Sept, 2006, .