2021· International Journal of Intelligent Automation & Robotics Engineering· Vol 4, pp. 01-15· 0 citations
TL;DR
A DNN-based vision-guided robotic assembly framework that integrates computer vision, intelligent decision-making, and real-time robotic control is proposed, supporting flexible automation and next-generation smart manufacturing in Industry 4.0 environments.
Abstract
Intelligent manufacturing is transforming traditional robotic assembly into adaptive, autonomous, and data-driven production systems. Conventional robotic assembly relies on pre-programmed trajectories, structured environments, and rule-based vision systems, limiting flexibility in handling complex components and uncertain operating conditions. This paper proposes a deep neural network (DNN)-based vision-guided robotic assembly framework that integrates computer vision, intelligent decision-making, and real-time robotic control. The framework consists of four modules: vision acquisition, deep feature learning, assembly intelligence, and robotic execution. Industrial cameras and depth sensors capture visual data, while convolutional neural networks (CNNs) perform object recognition and pose estimation. Extracted visual features are combined with motion planning to generate optimized assembly trajectories. Mathematical models for feature extraction, neural network optimization, and robotic coordinate transformation enhance system accuracy and reliability. Performance is evaluated using recognition accuracy, assembly precision, processing speed, adaptability, and operational efficiency. Experimental results demonstrate that the proposed framework outperforms conventional image processing and machine learning approaches, enabling accurate assembly of irregular components under uncertain conditions. The proposed approach enhances perception, autonomous decision-making, and intelligent adaptation, supporting flexible automation and next-generation smart manufacturing in Industry 4.0 environments.
Industry 4.0 has transformed conventional manufacturing into intelligent, automated, and connected production environments. Intelligent robotic pick-and-sort systems improve productivity, flexibility, product quality, and operational efficiency by overcoming the limitations of traditional rule-based automation. This paper presents an AI-enabled framework integrating computer vision, deep learning, robotic manipulation, edge computing, and the Industrial Internet of Things (IIoT) for dynamic manufacturing applications. Convolutional Neural Networks (CNNs) provide accurate object detection and classification, while intelligent motion planning and reinforcement learning optimize robotic grasping and movement. Sensor fusion, edge computing, and IIoT connectivity enable real-time monitoring, low-latency decision-making, and predictive maintenance, improving system reliability and reducing downtime. Performance is evaluated using object detection accuracy, sorting accuracy, processing time, throughput, energy efficiency, and overall system reliability. Compared with conventional automation, the proposed framework offers greater adaptability, higher sorting accuracy, and improved operational performance in dynamic production environments. The study concludes that intelligent robotic pick-and-sort systems are a key technology for smart factories, supporting flexible manufacturing, mass customization, and sustainable industrial production, with future opportunities in digital twins, explainable AI, cloud-edge intelligence, and collaborative human-robot systems.
Nandhini Ravi· International Journal of Int...· 0 citations
Robotic object recognition is a fundamental capability that enables autonomous robots to interact intelligently with dynamic environments. Traditional vision-based methods, such as SIFT, SURF, HOG, and template matching, perform well under controlled conditions but struggle with variations in lighting, viewpoint, occlusion, and complex backgrounds. Recent advances in deep learning have significantly improved recognition accuracy by automatically learning features from raw image data. However, individual deep learning models often face challenges related to computational cost, inference speed, and limited generalization. This paper proposes a Hybrid Deep Learning Framework that integrates Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), attention mechanisms, and multimodal sensor fusion (RGB, depth, and LiDAR) to enhance recognition accuracy and efficiency. The framework combines local and global feature extraction, adaptive feature fusion, intelligent object recognition, and robotic decision-making for real-time perception and task execution. It supports applications in industrial automation, warehouse logistics, autonomous mobile robots, healthcare, agriculture, and service robotics while improving robustness, scalability, and computational efficiency.
Seshagiri N, Mahabala H. N.· International Journal of Int...· 0 citations
A vision-guided robotic action generation framework that explicitly models the data flow from visual perception to robotic action execution and introduces a structured visual data extraction mechanism that interprets raw visual outputs into type-consistent, constraint-aware, and physically feasible motion parameters, enabling reliable perception-action coupling in industrial assembly systems.
Longxiang Huang, Jiaxin Dai, Tao Wang et al.· International Conference on...· 0 citations
The outcomes demonstrate the efficacy of combining edge intelligence with closed-loop robotic control by confirming consistent behavior throughout simulation and limited physical testing.
Xiaoming Liu, Wei Su, Jie Zhang et al.· Scientific Reports· 0 citations
Accurate object detection and recognition remain fundamental challenges in robot vision systems operating in complex environments. To improve detection accuracy, robustness, and computational efficiency, this study proposes a multi-stage collaborative optimization framework based on convolutional neural networks. A lightweight backbone architecture combining depthwise separable convolution and channel attention mechanisms is first designed to reduce computational complexity while preserving semantic representation capability. An adaptive feature pyramid network is then developed to enhance multi-scale feature fusion and improve small-target recognition performance. Furthermore, a GIoU-based localization optimization strategy and a joint knowledge-distillation–pruning framework are introduced to improve localization accuracy and model efficiency simultaneously. Experimental evaluations on public and self-constructed datasets demonstrate superior performance in terms of detection accuracy, real-time processing capability, and parameter reduction. The proposed framework provides an efficient solution for intelligent robotic perception and offers potential applications in machine vision, electromagnetic imaging interpretation, and autonomous sensing systems.
An intelligent vision-based autonomous robotic framework that integrates deep learning-based object detection with hybrid adaptive navigation for dynamic environments is proposed in this research. The proposed system addresses the challenges of real-time perception and robust navigation in unstructured settings by combining a convolutional neural network (CNN) for object detection with a hybrid control mechanism for motion planning. The CNN, implemented using a state-of-the-art architecture such as YOLO, processes visual input to identify obstacles and target objects, providing critical environmental awareness. Moreover, the hybrid navigation strategy merges reactive obstacle avoidance, achieved through algorithms like the Vector Field Histogram (VFH), with adaptive path planning using Rapidly-exploring Random Trees (RRT) to ensure both immediate collision avoidance and long-term goal convergence. The integration of these components enables the robotic system to dynamically adjust its navigation policy in response to environmental changes, thereby improving robustness and adaptability. The novelty of our approach lies in the seamless fusion of vision-based perception and adaptive control, which enhances the system’s capability to operate in complex, dynamic scenarios. Experimental validation demonstrates the effectiveness of the framework in real-world applications, highlighting its potential for deployment in autonomous vehicles, service robotics, and industrial automation. The proposed method offers a scalable and efficient solution for autonomous systems requiring high levels of situational awareness and adaptive decision-making.
K. A.· International Journal on Rob...· 0 citations
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