On Stochastic Self-Assembly of Underwater Robots

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

Thandiackal

Mordig

et al. 2017

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.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

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

Distributed Autonomous Robotic Systems

Khodr

Martinoli

2019

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.

Synthesizing Rulesets for Programmable Robotic Self-assembly: A Case Study Using Floating Miniaturized Robots

Lecture Notes in Computer Science

Platerrier

Waegeli

et al. 2016

Abstract. Programmable stochastic self-assembly of modular robots provides promising means to formation of structures at different scales. Formalisms based on graph grammars and rule-based approaches have been previously published for controlling the self-assembly process. While several rule-synthesis algorithms have been proposed, formal synthesis of rulesets has only been shown for self-assembly of abstract graphs. Rules deployed on robotic modules are typically tuned starting from their abstract graph counterparts or designed manually. In this work, we extend the graph grammar formalism and propose a new encoding of the robots internal states. This allows formulating formal methods capable of automatically deriving the rules based on the morphology of the robots, in particular the number of connectors. The derived rules are directly applicable to robotic modules with no further tuning. In addition, our method allows for a reduced complexity in the rulesets. In order to illustrate the application of our method, we extend two synthesis algorithms from the literature, namely Singleton and Linchpin, to synthesize rules applicable to our floating robots. A microscopic simulation framework is developed to study the performance and transient behavior of the two algorithms. Finally, employing the generated rulesets, we conduct experiments with our robotic platform to demonstrate several assemblies.