An Autonomous Robotic Platform for Manipulation and Inspection of Metallic Surfaces in Industry 4.0

Czimmermann, Tamás; Chiurazzi, Marcello; Milazzo, M.; Roccella, S.; Barbieri, Marco; Dario, P.; Oddo, Calogero Maria; Ciuti, Gastone

doi:10.1109/tase.2021.3122820

Cited by 18 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For simplicity, it is further linearized by the firstorder Taylor series expansion at each reference trajectory point (x r , y r , f r ) for system (2), and subsequently discretized by forward Eulerian, we can get the following discrete-time tracking error system:…”

Section: The Dynamics Model Of Wmrmentioning

confidence: 99%

TVIE-based fault-tolerant model predictive control for trajectory tracking of mobile robot

Zhu,

Ying,

Wang

2023

Preprint

View full text Add to dashboard Cite

This paper investigates the trajectory tracking control issue for linear parameter-varying (LPV) system of wheeled mobile robot (WMR) with actuator fault and constraints, where a time-varying intermediate estimator (TVIE)-based fault-tolerant model predictive control (MPC) method is proposed. A new estimation-based predictive model is designed by introducing the estimations of error state and fault from the TVIE. Moreover, compared with the existing traditional IE or extended state observer(ESO), the estimation performance of TVIE whose gain is optimized online based on an objective function is better. To guarantee stability and recursive feasibility, the bimodal MPC strategy is used, where a terminal penalty and a terminal constraint are included. Finally, the effectiveness and superiority of the proposed approach is illustrated by two experiments.

show abstract

Section: The Dynamics Model Of Wmrmentioning

confidence: 99%

TVIE-based fault-tolerant model predictive control for trajectory tracking of mobile robot

Zhu,

Ying,

Wang

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…To carry it out, a new parent is randomly generated from the search space. Then, the new parent replaces the worst parent, which is selected through the fitness Equation (7). Thus, if the new parent improves the fitness, the worst parent is replaced by the new parent.…”

Section: Multi-objective Optimization For Micro-scale Free-form Surfa...mentioning

confidence: 99%

“…Then, the fifth step was performed to mutate the parent P 1,2 , which was selected as the worst parent. Additionally, a new parent was randomly generated from the solution space to compute the fitness (7). In this case, the fitness was not improved and the parent was not mutated.…”

Section: Multi-objective Optimization For Micro-scale Free-form Surfa...mentioning

confidence: 99%

“…Then, the parent P 1,2 was chosen in random form to mutate the weight w 1,2 , which was randomly selected. Next, a new weight was replaced in the parent P 1,2 , and the fitness was computed via Equation (7). In this case, the fitness was improved; therefore, the weight w 1,2 was mutated.…”

Section: Multi-objective Optimization For Micro-scale Free-form Surfa...mentioning

confidence: 99%

“…Additionally, the surface modeling provides fundamental tools in industry 4.0 to represent computationally flat and free-form surfaces [4,5]. In the same way, the flat and free-form surface modeling at the micro-scale provides computational tools in industry 4.0 to represent micro-scale objects [6,7], where the computational tools are constructed via mathematical models to generate flat and free-form surfaces at the micro-scale. Actually, micro-scale flat and freeform surface modeling is performed via optical microscope systems based on gray-level image processing in industry 4.0 [8,9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

Rodríguez

2022

Energies

View full text Add to dashboard Cite

Industry 4.0 represents high-level methodologies to make intelligent, autonomous, and self-adaptable manufacturing systems. Additionally, the surface modeling technology has become a great tool in industry 4.0 for representing the surface point cloud. Thus, the micro-scale machining technology requires efficient models to represent micro-scale flat and free-form surfaces. Therefore, it is fundamental to perform surface modeling through artificial intelligence for representing small surfaces. This study addressed multi-objective optimization via genetic algorithms and micro laser line projection to accomplish surface models for representing micro-scale flat and free-form surfaces, where an optical microscope system retrieves micro-scale topography via micro laser line coordinates and the multi-objective optimization constructs the flat and free-form surface models through genetic algorithms and micro-scale topography. The multi-objective optimization determines the surface model parameters through exploration and exploitation, and the solution space is deduced via surface data. The surface model generated through the multi-objective optimization fit accurately to the micro-scale target surface. Thus, the proposed technique enhanced the fitting of micro-scale flat and free-form surface models, which were deduced via gray-level images of an optical microscope. This enhancement was validated by a discussion between the multi-objective optimization via genetic algorithms and the micro-scale surface modeling via optical microscope imaging systems.

show abstract

Systematic comparison of software agents and Digital Twins: differences, similarities, and synergies in industrial production

Reinpold,

Wagner,

Gehlhoff

et al. 2024

J Intell Manuf

View full text Add to dashboard Cite

To achieve a highly agile and flexible production, a transformational shift is envisioned whereby industrial production systems evolve to be more decentralized, interconnected, and intelligent. Within this vision, production assets collaborate with each other, exhibiting a high degree of autonomy. Furthermore, information about individual production assets is accessible throughout their entire life-cycles. To realize this vision, the use of advanced information technology is required. Two commonly applied software paradigms in this context are Software Agents (referred to as Agents) and Digital Twins (DTs). This work presents a systematic comparison of Agents and DTs in industrial applications. The goal of the study is to determine the differences, similarities, and potential synergies between the two paradigms. The comparison is based on the purposes for which Agents and DTs are applied, the properties and capabilities exhibited by these software paradigms, and how they can be allocated within the Reference Architecture Model Industry 4.0. The comparison reveals that Agents are commonly employed in the collaborative planning and execution of production processes, while DTs are generally more applied to monitor production resources and process information. Although these observations imply characteristic sets of capabilities and properties for both Agents and DTs, a clear and definitive distinction between the two paradigms cannot be made. Instead, the analysis indicates that production assets utilizing a combination of Agents and DTs would demonstrate high degrees of intelligence, autonomy, sociability, and fidelity. To achieve this, further standardization is required, particularly in the field of DTs.

show abstract

An Autonomous Robotic Platform for Manipulation and Inspection of Metallic Surfaces in Industry 4.0

Cited by 18 publications

References 29 publications

TVIE-based fault-tolerant model predictive control for trajectory tracking of mobile robot

TVIE-based fault-tolerant model predictive control for trajectory tracking of mobile robot

Multi-Objective Optimization via GA Based on Micro Laser Line Scanning Data for Micro-Scale Surface Modeling

Systematic comparison of software agents and Digital Twins: differences, similarities, and synergies in industrial production

Contact Info

Product

Resources

About