Cultivated Microorganisms Control a Real Robot: A Model of Dynamical Coupling between Internal Growth and Robot Movement

2009 ◽  
pp. 1087-1094 ◽  
Author(s):  
Hiroaki Wagatsuma
Keyword(s):  
Dynamical Coupling ◽  
Real Robot

Dynamical coupling of ion adsorption with fluid flow in nanopores

AIChE Journal ◽  
2021 ◽  
Author(s):  
Teng Zhao ◽  
Leying Qing ◽  
Ting Long ◽  
Xiaofei Xu ◽  
Shuangliang Zhao ◽  
...  
Keyword(s):  
Fluid Flow ◽  
Ion Adsorption ◽  
Dynamical Coupling

High speed long-term visual object tracking algorithm for real robot systems

2021 ◽  
Vol 434 ◽  
pp. 268-284
Author(s):  
Muxi Jiang ◽  
Rui Li ◽  
Qisheng Liu ◽  
Yingjing Shi ◽  
Esteban Tlelo-Cuautle
Keyword(s):  
Object Tracking ◽  
High Speed ◽  
Tracking Algorithm ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Robot Systems ◽  
Real Robot

Fuzzy Local Navigation of a simulated real robot in unknown environments

2005 ◽  
Author(s):  
J. Gasos ◽  
M.C. Garcia-Alegre ◽  
R.G. Rosa
Keyword(s):  
Unknown Environments ◽  
Local Navigation ◽  
Real Robot

Parameterization of Eddy Fluxes near Oceanic Boundaries

2008 ◽  
Vol 21 (12) ◽  
pp. 2770-2789 ◽  
Author(s):  
Raffaele Ferrari ◽  
James C. McWilliams ◽  
Vittorio M. Canuto ◽  
Mikhail Dubovikov
Keyword(s):  
Boundary Layer ◽  
Transition Layer ◽  
General Circulation ◽  
Mesoscale Eddies ◽  
Turbulent Boundary Layers ◽  
Mesoscale Variability ◽  
Transition Layers ◽  
Boundary Layer Turbulence ◽  
Dynamical Coupling ◽  
Eddy Fluxes

Abstract In the stably stratified interior of the ocean, mesoscale eddies transport materials by quasi-adiabatic isopycnal stirring. Resolving or parameterizing these effects is important for modeling the oceanic general circulation and climate. Near the bottom and near the surface, however, microscale boundary layer turbulence overcomes the adiabatic, isopycnal constraints for the mesoscale transport. In this paper a formalism is presented for representing this transition from adiabatic, isopycnally oriented mesoscale fluxes in the interior to the diabatic, along-boundary mesoscale fluxes near the boundaries. A simple parameterization form is proposed that illustrates its consequences in an idealized flow. The transition is not confined to the turbulent boundary layers, but extends into the partially diabatic transition layers on their interiorward edge. A transition layer occurs because of the mesoscale variability in the boundary layer and the associated mesoscale–microscale dynamical coupling.

Remote robotic assembly guided by 3D models linking to a real robot

CIRP Annals ◽  
2014 ◽  
Vol 63 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Lihui Wang ◽  
Abdullah Mohammed ◽  
Mauro Onori
Keyword(s):  
3D Models ◽  
Robotic Assembly ◽  
Real Robot

scholarly journals Generalization-Based Acquisition of Training Data for Motor Primitive Learning by Neural Networks

2021 ◽  
Vol 11 (3) ◽  
pp. 1013
Author(s):  
Zvezdan Lončarević ◽  
Rok Pahič ◽  
Aleš Ude ◽  
Andrej Gams
Keyword(s):  
Neural Networks ◽  
Dimensionality Reduction ◽  
Gaussian Process Regression ◽  
Search Space ◽  
Robot Learning ◽  
Training Data ◽  
Practical Applications ◽  
Latent Space ◽  
Real Robot ◽  
Low Dimensional

Autonomous robot learning in unstructured environments often faces the problem that the dimensionality of the search space is too large for practical applications. Dimensionality reduction techniques have been developed to address this problem and describe motor skills in low-dimensional latent spaces. Most of these techniques require the availability of a sufficiently large database of example task executions to compute the latent space. However, the generation of many example task executions on a real robot is tedious, and prone to errors and equipment failures. The main result of this paper is a new approach for efficient database gathering by performing a small number of task executions with a real robot and applying statistical generalization, e.g., Gaussian process regression, to generate more data. We have shown in our experiments that the data generated this way can be used for dimensionality reduction with autoencoder neural networks. The resulting latent spaces can be exploited to implement robot learning more efficiently. The proposed approach has been evaluated on the problem of robotic throwing at a target. Simulation and real-world results with a humanoid robot TALOS are provided. They confirm the effectiveness of generalization-based database acquisition and the efficiency of learning in a low-dimensional latent space.

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