The fast paced nature of robotic soccer necessitates real time sensing coupled with quick behaving and decision making. In the field with real robots, it is important to well perceive the location of ball, team robots...The fast paced nature of robotic soccer necessitates real time sensing coupled with quick behaving and decision making. In the field with real robots, it is important to well perceive the location of ball, team robots and opponent robots through the vision system in real time. In this paper the architecture of global vision system of our small size robotic team and the process of object recognition is described. According to the study on color distribution in different color space and quantitative investigation, a method which uses H (Hue) thresholds as the major thresholds to feature exact and recognize object in real time is presented.展开更多
Artificial intelligence of things systems equipped with flexible sensors can autonomously and intelligently detect the condition of the surroundings.However,current intelligent monitoring systems always rely on an ext...Artificial intelligence of things systems equipped with flexible sensors can autonomously and intelligently detect the condition of the surroundings.However,current intelligent monitoring systems always rely on an external computer with the capability of machine learning rather than integrating it into the sensing device.The computer-assisted intelligent system is hampered by energy inefficiencies,privacy issues,and bandwidth restrictions.Here,a flexible,large-scale sensing array with the capability of low-power in-sensor intelligence based on a compression hypervector encoder is proposed for real-time recognition.The system with in-sensor intelligence can accommodate different individuals and learn new postures without additional computer processing.Both the communication bandwidth requirement and energy consumption of this system are significantly reduced by 1,024 and 500 times,respectively.The capability for in-sensor inference and learning eliminates the necessity to transmit raw data externally,thereby effectively addressing privacy concerns.Furthermore,the system possesses a rapid recognition speed(a few hundred milliseconds)and a high recognition accuracy(about 99%),comparing with support vector machine and other hyperdimensional computing methods.The research holds marked potential for applications in the integration of artificial intelligence of things and flexible electronics.展开更多
文摘The fast paced nature of robotic soccer necessitates real time sensing coupled with quick behaving and decision making. In the field with real robots, it is important to well perceive the location of ball, team robots and opponent robots through the vision system in real time. In this paper the architecture of global vision system of our small size robotic team and the process of object recognition is described. According to the study on color distribution in different color space and quantitative investigation, a method which uses H (Hue) thresholds as the major thresholds to feature exact and recognize object in real time is presented.
基金supported by the National Key R&D Program of China(grant no.2020YFA0405700)the NationalNatural Science Foundation of China(grant no.51925503 to Y.H.and grant no.52375568 to F.Z.)+1 种基金the Tencent Foundation(XPLORER Prize to Y.H.)the Science and Technology Innovation Team of Hubei Province.
文摘Artificial intelligence of things systems equipped with flexible sensors can autonomously and intelligently detect the condition of the surroundings.However,current intelligent monitoring systems always rely on an external computer with the capability of machine learning rather than integrating it into the sensing device.The computer-assisted intelligent system is hampered by energy inefficiencies,privacy issues,and bandwidth restrictions.Here,a flexible,large-scale sensing array with the capability of low-power in-sensor intelligence based on a compression hypervector encoder is proposed for real-time recognition.The system with in-sensor intelligence can accommodate different individuals and learn new postures without additional computer processing.Both the communication bandwidth requirement and energy consumption of this system are significantly reduced by 1,024 and 500 times,respectively.The capability for in-sensor inference and learning eliminates the necessity to transmit raw data externally,thereby effectively addressing privacy concerns.Furthermore,the system possesses a rapid recognition speed(a few hundred milliseconds)and a high recognition accuracy(about 99%),comparing with support vector machine and other hyperdimensional computing methods.The research holds marked potential for applications in the integration of artificial intelligence of things and flexible electronics.
