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 92
Special Issue Editors
Interests: action recognition; autonomous vehicle
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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