Industrial Robotic Neurotwin-Roboflow: A Bio-Inspired Digital Twin Control Framework With Real-Time Adaptive Flow Optimization For Industrial Robotic Automation

The dynamic Industrial automation requires intelligent control systems with the ability to challenge the dynamic circumstances on a real time basis. NeuroTwin-RoboFlow has proposed a new bio-inspired controller that combines the digital twin technology and real-time adaptive feedback flow optimization to achieve higher accuracy and efficiency of robotic automation process in industry. The framework is fundamentally embedded with an innovative breakthrough concept in the year 2024, which is characterized by a Bio-Adaptive Fuzzy Control Algorithm (BAFCA). It is a hybrid model that merges learning like an interposer that merges neural, fuzzy, and evolutionary approaches to control. This combination approach can be highly accurate in predicting, detecting anomalies and optimize in real-time the movement of robots and task accomplishment. This is a neuro-symbolic level that includes a digital twin that replicates real-time robotic conditions and environmental variables to make predictive modelling of complicated operative choices. The streams of real-time data form industrial robots are adjusted to the virtual twin providing an opportunity to respond to mechanical variability, the change of loads, and environmental unpredictability’s on the fly. The BAFCA module that has been embedded is very fast in convergence to optimal control signals, with little latency time and high throughput of 96.8 rate of accuracy in the real-life experiments. Also the application of adaptive flow optimizer dynamically balances control pathways so as to have energy efficient operation even as accuracy of task performance remains intact. The experiment conducted on the prototypes of smart factories showed that the response time, error and collaborative task processing are much improved with regard to the other systems. This paradigm provides a scalable and smart control paradigm with future-proof robotics in the next-generation Industry 5.0, allowing decision-making, resilience, and autonomy of the robot.

Authors

Publication Details

Journal
International Journal of Environmental Sciences
Published
2025-07-26
DOI
https://doi.org/10.64252/ar4bm508
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
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article

Industrial Robotic Neurotwin-Roboflow: A Bio-Inspired Digital Twin Control Framework With Real-Time Adaptive Flow Optimization For Industrial Robotic Automation

Ravi Kumar Saidala
International Journal of Environmental Sciences
Digital Transformation in Industry
article

Industrial Robotic Neurotwin-Roboflow: A Bio-Inspired Digital Twin Control Framework With Real-Time Adaptive Flow Optimization For Industrial Robotic Automation

Ravi Kumar Saidala
article en

Abstract

The dynamic Industrial automation requires intelligent control systems with the ability to challenge the dynamic circumstances on a real time basis. NeuroTwin-RoboFlow has proposed a new bio-inspired controller that combines the digital twin technology and real-time adaptive feedback flow optimization to achieve higher accuracy and efficiency of robotic automation process in industry. The framework is fundamentally embedded with an innovative breakthrough concept in the year 2024, which is characterized by a Bio-Adaptive Fuzzy Control Algorithm (BAFCA). It is a hybrid model that merges learning like an interposer that merges neural, fuzzy, and evolutionary approaches to control. This combination approach can be highly accurate in predicting, detecting anomalies and optimize in real-time the movement of robots and task accomplishment. This is a neuro-symbolic level that includes a digital twin that replicates real-time robotic conditions and environmental variables to make predictive modelling of complicated operative choices. The streams of real-time data form industrial robots are adjusted to the virtual twin providing an opportunity to respond to mechanical variability, the change of loads, and environmental unpredictability’s on the fly. The BAFCA module that has been embedded is very fast in convergence to optimal control signals, with little latency time and high throughput of 96.8 rate of accuracy in the real-life experiments. Also the application of adaptive flow optimizer dynamically balances control pathways so as to have energy efficient operation even as accuracy of task performance remains intact. The experiment conducted on the prototypes of smart factories showed that the response time, error and collaborative task processing are much improved with regard to the other systems. This paradigm provides a scalable and smart control paradigm with future-proof robotics in the next-generation Industry 5.0, allowing decision-making, resilience, and autonomy of the robot.

International Journal of Environmental Sciences
Openalex Percentile: Top 73%
Digital Transformation in Industry
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Industrial Robotic Neurotwin-Roboflow: A Bio-Inspired Digital Twin Control Framework With Real-Time Adaptive Flow Optimization For Industrial Robotic Automation — Ravi Kumar Saidala · International Journal of Environmental Sciences (2025) | TGRS Research Map | TGRS