With the continuous development of artificial intelligence and robot technology, robots are being applied more and more widely in real-life situations. In manufacturing, robots can perform repetitive, high-intensity, and dangerous tasks, improving production efficiency and quality. In the logistics and warehousing field, robots can achieve automated handling of goods and warehouse management, reducing labor costs and improving efficiency. In the field of healthcare, robots can assist doctors in surgeries and treatments, improving surgical accuracy and treatment effectiveness. In the field of home services, robots can help the elderly and the disabled to complete some activities in daily life and improve the quality of life. In the military field, robots can accomplish some dangerous tasks and reduce the casualties of soldiers. Robots need to be able to perceive multiple forms of information in their surroundings and extract useful knowledge to make the right decisions. However, in real scenarios, robots often need to simultaneously process information from multiple sensors, and effectively fuse and utilize the information. For example, in autonomous vehicles, robots need to simultaneously process information from multiple sensors, such as lidar, cameras and GPS, to achieve precise navigation and obstacle avoidance. In industrial robots, the robot needs to simultaneously process information from multiple sensors, such as vision, force and position sensors, to achieve high-precision positioning and operation. In medical robots, the robot needs to simultaneously process information from multiple sensors, such as ultrasonic, optical and force sensors, to achieve accurate surgery and treatment. Therefore, the research on multi-modal information fusion and decision-making technology for robots is of great significance.
This research topic will provide a platform for researchers to exchange and share the latest achievements of multi-modal information fusion and decision-making technology for robots and provide some inspiration and references for scholars in related fields. This research topic will also help to promote the development of multi-modal information fusion and decision-making technology for robots as well as the intersection and integration of the field.
Topics to be received include but are not limited to:
- Fusion and processing of multi-modal information for robot perception
- Application of multi-sensor data fusion in robot decision-making
- Robot multi-modal information fusion technology based on deep learning
- Multimodal Image Processing for robot
- Research on the relationship between multi-modal information and decision-making of robot
- Sentiment analysis and robot decision-making in multi-modal interaction
- Application of robot multi-modal information fusion in intelligent manufacturing, medical and other fields
- Robot multi-modal information fusion algorithm
- Application of robot multi-modal information fusion in navigation and positioning
- Application of robot multi-modal information fusion in human-machine interaction
- Robot multi-modal information perception and recognition technology
With the continuous development of artificial intelligence and robot technology, robots are being applied more and more widely in real-life situations. In manufacturing, robots can perform repetitive, high-intensity, and dangerous tasks, improving production efficiency and quality. In the logistics and warehousing field, robots can achieve automated handling of goods and warehouse management, reducing labor costs and improving efficiency. In the field of healthcare, robots can assist doctors in surgeries and treatments, improving surgical accuracy and treatment effectiveness. In the field of home services, robots can help the elderly and the disabled to complete some activities in daily life and improve the quality of life. In the military field, robots can accomplish some dangerous tasks and reduce the casualties of soldiers. Robots need to be able to perceive multiple forms of information in their surroundings and extract useful knowledge to make the right decisions. However, in real scenarios, robots often need to simultaneously process information from multiple sensors, and effectively fuse and utilize the information. For example, in autonomous vehicles, robots need to simultaneously process information from multiple sensors, such as lidar, cameras and GPS, to achieve precise navigation and obstacle avoidance. In industrial robots, the robot needs to simultaneously process information from multiple sensors, such as vision, force and position sensors, to achieve high-precision positioning and operation. In medical robots, the robot needs to simultaneously process information from multiple sensors, such as ultrasonic, optical and force sensors, to achieve accurate surgery and treatment. Therefore, the research on multi-modal information fusion and decision-making technology for robots is of great significance.
This research topic will provide a platform for researchers to exchange and share the latest achievements of multi-modal information fusion and decision-making technology for robots and provide some inspiration and references for scholars in related fields. This research topic will also help to promote the development of multi-modal information fusion and decision-making technology for robots as well as the intersection and integration of the field.
Topics to be received include but are not limited to:
- Fusion and processing of multi-modal information for robot perception
- Application of multi-sensor data fusion in robot decision-making
- Robot multi-modal information fusion technology based on deep learning
- Multimodal Image Processing for robot
- Research on the relationship between multi-modal information and decision-making of robot
- Sentiment analysis and robot decision-making in multi-modal interaction
- Application of robot multi-modal information fusion in intelligent manufacturing, medical and other fields
- Robot multi-modal information fusion algorithm
- Application of robot multi-modal information fusion in navigation and positioning
- Application of robot multi-modal information fusion in human-machine interaction
- Robot multi-modal information perception and recognition technology
