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Advances in Computer Vision and Artificial Intelligence Technologies for Industrial Robotics

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 393

Special Issue Editors


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Guest Editor
Faculty of Engineering and Applied Sciences, Cranfield University, Cranfield MK43 0AL, UK
Interests: action recognition; autonomous vehicle

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Guest Editor
Department of Engineering, Durham University, Durham DH1 3LE, UK
Interests: machine learning; signal processing for wireless communications; UAV control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of artificial intelligence (AI), the Internet of Things (IoT), and robotics has significantly accelerated the digital transformation of industries and enhanced productivity in manufacturing, logistics, and other sectors. By moving towards higher levels of intelligence, Industry 5.0 places greater emphasis on efficient human–robot collaboration, which poses new challenges to the ability of intelligent industrial robots to perceive, reason, and act in dynamic and complex environments. In the meantime, ensuring the trustworthiness, security, and robustness of AI-driven systems has become a key issue for practical implementation that is essential for improving the public trust and acceptance of these technologies.

This Special Issue will present the latest research and innovations at the intersection of computer vision, multi-modal sensing, and AI-based decision making and industrial robotics, as well as explore trustworthy, secure, reliable, AI-driven system design and development for achieving wider public acceptance and technology adoption.

We invite original contributions that address fundamental, theoretical, and practical aspects of the following topics:

  • Multi-Modal Sensing and Fusion: Algorithms and architectures used for integrating data from heterogeneous sensors, such as LiDAR, cameras, radar, and IMUs, to enhance perception and decision-making in robotics and autonomous systems;
  • Computer Vision for Robotics: Vision-based navigation, mapping, object detection, and tracking in dynamic and unstructured environments;
  • Decision-making for Robotics: Approaches such as deep neural networks, reinforcement learning, imitation learning, large language models, etc., used for diverse robotic tasks;
  • Human–machine interaction: Frameworks or practices for efficient human–robot interaction in industrial applications;
  • Trustworthy AI in Sensor Systems: Approaches to ensure transparency, fairness, robustness, and accountability in AI-driven sensor applications;
  • AI Security and Privacy: Techniques to mitigate adversarial attacks, ensure data integrity, and preserve privacy in sensor networks and robotic systems;
  • Applications in Robotics and Beyond: Real-world implementations in autonomous vehicles, drones, industrial automation, and healthcare robotics.

Dr. Lichao Yang
Dr. Zhuangkun Wei
Guest Editors

Manuscript Submission Information

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Keywords

  • multi-modal sensing and fusion
  • computer vision for robotics
  • decision-making for robotics
  • human–machine interaction
  • trustworthy AI in sensor systems
  • AI security and privacy
  • applications in robotics

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Published Papers (1 paper)

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Research

22 pages, 8008 KiB  
Article
Real-Time Detection and Localization of Force on a Capacitive Elastomeric Sensor Array Using Image Processing and Machine Learning
by Peter Werner Egger, Gidugu Lakshmi Srinivas and Mathias Brandstötter
Sensors 2025, 25(10), 3011; https://doi.org/10.3390/s25103011 - 10 May 2025
Viewed by 267
Abstract
Soft and flexible capacitive tactile sensors are vital in prosthetics, wearable health monitoring, and soft robotics applications. However, achieving accurate real-time force detection and spatial localization remains a significant challenge, especially in dynamic, non-rigid environments like prosthetic liners. This study presents a real-time [...] Read more.
Soft and flexible capacitive tactile sensors are vital in prosthetics, wearable health monitoring, and soft robotics applications. However, achieving accurate real-time force detection and spatial localization remains a significant challenge, especially in dynamic, non-rigid environments like prosthetic liners. This study presents a real-time force point detection and tracking system using a custom-fabricated soft elastomeric capacitive sensor array in conjunction with image processing and machine learning techniques. The system integrates Otsu’s thresholding, Connected Component Labeling, and a tailored cluster-tracking algorithm for anomaly detection, enabling real-time localization within 1 ms. A 6×6 Dragon Skin-based sensor array was fabricated, embedded with copper yarn electrodes, and evaluated using a UR3e robotic arm and a Schunk force-torque sensor to generate controlled stimuli. The fabricated tactile sensor measures the applied force from 1 to 3 N. Sensor output was captured via a MUCA breakout board and Arduino Nano 33 IoT, transmitting the Ratio of Mutual Capacitance data for further analysis. A Python-based processing pipeline filters and visualizes the data with real-time clustering and adaptive thresholding. Machine learning models such as linear regression, Support Vector Machine, decision tree, and Gaussian Process Regression were evaluated to correlate force with capacitance values. Decision Tree Regression achieved the highest performance (R2=0.9996, RMSE=0.0446), providing an effective correlation factor of 51.76 for force estimation. The system offers robust performance in complex interactions and a scalable solution for soft robotics and prosthetic force mapping, supporting health monitoring, safe automation, and medical diagnostics. Full article
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