Research Topic

Theory and Application of Intelligent Vision Systems for Mobile and Humanoid Robotics

About this Research Topic

The study of robot intelligence explores adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. Advances aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low-cost solutions. These techniques are now being used widely by field engineers to solve a whole range of hitherto intractable problems, especially in robotic society.

One particular focus of robot intelligence is the introduction of intelligent vision systems for recognition. Recognition systems are essential for robots to be able to function in complex and changing environments. Many researchers have started to pay attention to how mobile and humanoid robots will be applied to the real world, and how we can best enable the transition. As an example, traffic sign recognition tasks are currently being explored in detail as traffic sign recognition will play a vital role in the future of robotic applications in settings such as public roads, high streets or industrial settings.

The aim of this Research Topic is to bring together academics and industrial practitioners to exchange and discuss the latest innovations and applications of intelligent vision systems in robotics. This Research Topic is specifically devoted to new activities in computer vision for mobile and humanoid robots and welcomes submissions related to the real-world applications of such devices.

Relevant themes include, but are not limited to:

• Computer vision for mobile and humanoid robotic systems
• Artificial intelligence and algorithms for intelligent recognition systems
• Traffic sign detection and classification for robotic systems
• Using traffic sign recognition for robot task planning
• Fuzzy logic or deep learning based recognition systems
• Smart systems in machine vision for robotic systems
• Real-world applications of robotic recognition systems


Keywords: Deep learning, machine vision, recognition, Convolutional neural network, Sign recognition, Robot Visual Systems, Intelligent Vision Systems, Humanoid Robots


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The study of robot intelligence explores adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. Advances aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low-cost solutions. These techniques are now being used widely by field engineers to solve a whole range of hitherto intractable problems, especially in robotic society.

One particular focus of robot intelligence is the introduction of intelligent vision systems for recognition. Recognition systems are essential for robots to be able to function in complex and changing environments. Many researchers have started to pay attention to how mobile and humanoid robots will be applied to the real world, and how we can best enable the transition. As an example, traffic sign recognition tasks are currently being explored in detail as traffic sign recognition will play a vital role in the future of robotic applications in settings such as public roads, high streets or industrial settings.

The aim of this Research Topic is to bring together academics and industrial practitioners to exchange and discuss the latest innovations and applications of intelligent vision systems in robotics. This Research Topic is specifically devoted to new activities in computer vision for mobile and humanoid robots and welcomes submissions related to the real-world applications of such devices.

Relevant themes include, but are not limited to:

• Computer vision for mobile and humanoid robotic systems
• Artificial intelligence and algorithms for intelligent recognition systems
• Traffic sign detection and classification for robotic systems
• Using traffic sign recognition for robot task planning
• Fuzzy logic or deep learning based recognition systems
• Smart systems in machine vision for robotic systems
• Real-world applications of robotic recognition systems


Keywords: Deep learning, machine vision, recognition, Convolutional neural network, Sign recognition, Robot Visual Systems, Intelligent Vision Systems, Humanoid Robots


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

30 November 2021 Abstract
10 January 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

30 November 2021 Abstract
10 January 2022 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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