Results 71 to 80 of about 31,914 (258)

Coupled-Error-Based Formation Control for Rapid Formation Completion by Omni-Directional Robots

open access: yesApplied Sciences
This paper proposes a coupled-error-based formation control algorithm for the rapid formation completion of multi-robot systems. We consider a multi-robot system with omni-directional robots with swerve-driving mechanisms and a communication system with ...
Ji-Wook Kwon   +6 more
doaj   +1 more source

Multi-robot formation control based on high-order bilateral consensus

open access: yesMeasurement + Control, 2020
A high-order bilateral consensus robot formation control protocol for multi-agent systems is proposed in this paper. Considering the relationship between the state of the information exchange topology and derivatives, a third-order bilateral consistency ...
Dejian Liu   +5 more
doaj   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

open access: yesAdvanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch   +3 more
wiley   +1 more source

Real Performance Evaluation On MQTT and COAP Protocol in Ubiquitous Network Robot Platform (UNRPF) for Disaster Multi-robot Communication

open access: yesEmitter: International Journal of Engineering Technology, 2018
Disaster multi-robot has a significant role in a disaster area to do many tasks like detection of fire, search and rescue of victims, etc. It needs to build good communication between the operator and multi-robot and among multi-robot themselves to ...
Muhammad Ikrar Yamin   +2 more
doaj   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

open access: yesAdvanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang   +2 more
wiley   +1 more source

Microstructure Evolution of a VMnFeCoNi High‐Entropy Alloy After Synthesis, Swaging, and Annealing

open access: yesAdvanced Engineering Materials, EarlyView.
The synthesis and processing (rotary swaging and annealing) of the novel VMnFeCoNi alloy is investigated, alongside the estimation of the grain size effect on hardness. Analysis of a wide grain size range of recrystallized microstructures (12–210 µm) reveals a low annealing twin density.
Aditya Srinivasan Tirunilai   +6 more
wiley   +1 more source

A hardware-based modeling approach for real world collaborative multi-robot tasks

open access: yesPaladyn, 2016
This article describes an implementation of a hardware-based modeling system for multi-robot collaborative tasks. Authors propose to perform a hardware-based modeling phase as an intermediate step between software simulation and implementation within a ...
Alexandrov Vladimir   +2 more
doaj   +1 more source

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

open access: yesAdvanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann   +8 more
wiley   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

open access: yesAdvanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer   +4 more
wiley   +1 more source

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