Lily: A miniature floating robotic platform for programmable stochastic self-assembly

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Martinoli

2016

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Abstract-Several self-assembly systems have been developed in recent years, where depending on the capabilities of the building blocks and the controlability of the environment, the assembly process is guided typically through either a fully centralized or a fully distributed control approach. In this work, we present a novel experimental system for studying the range of fully centralized to fully distributed control strategies. The system is built around the floating 3-cm-sized Lily robots, and comprises a water-filled tank with peripheral pumps, an overhead camera, an overhead projector, and a workstation capable of controlling the fluidic flow field, setting the ambient luminosity, communicating with the robots over radio, and visually tracking their trajectories. We carry out several experiments to characterize the system and validate its capabilities. First, a statistical analysis is conducted to show that the system is governed by reaction diffusion dynamics, and validate the applicability of the standard chemical kinetics modeling. Additionally, the natural tendency of the system for structure formation subject to different flow fields is investigated and corresponding implications on guiding the self-assembly process are discussed. Finally, two control approaches are studied: 1) a fully distributed control approach and 2) a distributed approach with additional central supervision exhibiting an improved performance. The formation time statistics are compared and a discussion on the generalization of the method is provided.

Section: B Natural Signature Of the Systemmentioning

confidence: 99%

Section: B Lily Robotsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Characterization and validation of a novel robotic system for fluid-mediated programmable stochastic self-assembly

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Martinoli

2016

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“…Our system consists of two main components: 1) the floating Lily robotic modules [20], and 2) the experimental setup built around them [7]. The Lilies are endowed with four custom-designed Electro-Permanent Magnets (EPM) to latch and also to communicate locally with their neighbors.…”

Section: Self-assembly In Our Robotic Systemmentioning

confidence: 99%

Probabilistic modeling of programmable stochastic self-assembly of robotic modules

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Thandiackal

Mordig

et al. 2017

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Abstract-Creating accurate models of stochastically selfassembling systems is a key step in developing control strategies, centralized or distributed, for the self-assembly process. This paper comparatively studies several aspects of developing probabilistic models for programmable self-assembling systems of stochastically interacting modules. In particular, we systematically investigate Markov models as well as hidden Markov models to predict the self-assembly process dynamics. We consider a case study leveraging our fluidic self-assembly robotic system. The ground truth is obtained through a high-fidelity simulation, calibrated using real experimental data. We first consider Markov models and employ the formalism of chemical reaction networks. In order to compute the model parameters, i.e. the reaction rates in the network, three different methods are studied. We then investigate the validity of the underlying well-mixed assumption, and thus the Markov property, for our system through estimation of the diffusion coefficient, through two different approaches. The system is shown to be borderline well-mixed, motivating extension of the initial Markov models to more complex models in order to achieve improved model accuracy. We formulate an automatic method for creating a hidden Markov model starting from a Markov model, based on a previously existing systematic method. Sample trajectories of the models are realized using the Gillespie's method. The resulting hidden Markov model is shown to achieve an improved accuracy over the standard Markov model.

Design and Calibration of a Lightweight Physics-Based Model for Fluid-Mediated Self-assembly of Robotic Modules

Distributed Autonomous Robotic Systems

Khodr

Martinoli

2019

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In this paper, we consider a system consisting of multiple floating robotic modules performing self-assembly. Faithfully modeling such a system and its inter-module interactions typically involves capturing the hydrodynamic forces acting on the modules using computationally expensive fluid dynamic modeling tools. This poses restrictions on the usability of the resulting models. Here, we present a new approach towards modeling such systems. First, we show how the hardware and firmware of the robotic modules 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. Our calibration method employs a Particle Swarm Optimization (PSO) algorithm, and consists of minimizing the difference between Mean Squared Displacement (MSD) data extracted from real and simulated trajectories of multiple robotic modules.