About this Research Topic
Automation has been an increasing demand in the 21st century, with robots’ rapid growth in areas such as logistics, food delivery, manufacturing, and warehousing. As human interaction with robots increases, so do the requirements for safety and control in these encounters. Thanks to advances in deep learning, especially convolutional neural networks, robots can now perform tasks such as object detection and classification with unprecedented accuracy. Semantic segmentation allows robots to understand the entire scene and make more informed decisions, while pose estimation facilitates the precise manipulation of objects. Recent studies have shown significant advancements in sensor technologies, including LiDAR, imaging/vision sensors (cameras), tactile sensors, and ultrasonic sensors, which are pivotal in enhancing robotic safety and control. Furthermore, transfer learning has accelerated the development of sensor models, enabling robots to adapt quickly to new environments and tasks. The combination of computer vision and natural language processing has resulted in robots capable of responding to voice commands and maintaining natural conversations, opening up new possibilities for human-robot interaction.
However, despite these advancements, there remain gaps in the integration and optimization of these sensors, particularly in dynamic and unpredictable environments. The need for more comprehensive research to address these gaps is evident, as current solutions often fall short in real-world applications.
This Research Topic aims to explore the use of vision sensors, particularly cameras, to achieve safety and accurate control in robotics and to improve the robustness of vision systems, exploring new neural network architectures, and developing more efficient algorithms for image processing. The main objectives include investigating how vision sensors can be optimized for better collision detection, obstacle avoidance, and navigation. Additionally, the research will seek to answer specific questions such as: How can machine vision technology be further advanced to improve robotic safety? What are the limitations of current vision sensors in dynamic environments? By addressing these questions, the research aims to contribute to the development of more reliable and efficient robotic systems.
To gather further insights in the integration and optimization of vision sensors for robotic safety and control, we welcome articles addressing, but not limited to, the following themes:
- Cameras
- LiDAR
- Imaging sensors
- Tactile sensors
- Ultrasonic sensors
- Machine vision technology
- Deep learning techniques
- Sensor fusion techniques
- Real-time data processing
- Obstacle detection and avoidance
- Navigation and localization systems
However, despite these advancements, there remain gaps in the integration and optimization of these sensors, particularly in dynamic and unpredictable environments. The need for more comprehensive research to address these gaps is evident, as current solutions often fall short in real-world applications.
This Research Topic aims to explore the use of vision sensors, particularly cameras, to achieve safety and accurate control in robotics and to improve the robustness of vision systems, exploring new neural network architectures, and developing more efficient algorithms for image processing. The main objectives include investigating how vision sensors can be optimized for better collision detection, obstacle avoidance, and navigation. Additionally, the research will seek to answer specific questions such as: How can machine vision technology be further advanced to improve robotic safety? What are the limitations of current vision sensors in dynamic environments? By addressing these questions, the research aims to contribute to the development of more reliable and efficient robotic systems.
To gather further insights in the integration and optimization of vision sensors for robotic safety and control, we welcome articles addressing, but not limited to, the following themes:
- Cameras
- LiDAR
- Imaging sensors
- Tactile sensors
- Ultrasonic sensors
- Machine vision technology
- Deep learning techniques
- Sensor fusion techniques
- Real-time data processing
- Obstacle detection and avoidance
- Navigation and localization systems
Keywords: Sensors, safety, control, cameras, detection
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.
