A system is described here that can noninvasively control the navigation of freely behaving rat via ultrasonic,epidermaland LED photic stimulators on the back.The system receives commands from a remote host computer t...A system is described here that can noninvasively control the navigation of freely behaving rat via ultrasonic,epidermaland LED photic stimulators on the back.The system receives commands from a remote host computer to deliver specifiedelectrical stimulations to the hearing,pain and visual senses of the rat respectively.The results demonstrate that the three stimuliwork in groups for the rat navigation.We can control the rat to proceed and make right and left turns with great efficiency.Thisexperiment verified that the rat was able to reach a setting destination in the way of cable with the help of a person through theappropriate coordination of the three stimulators.The telemetry video camera mounted on the head of the rat also achieveddistant image acquisition and helped to adjust its navigation path over a distance of 300 m.In a word,the non-invasive motioncontrol navigation system is a good,stable and reliable bio-robot.展开更多
针对多自主水下航行器(Autonomous Underwater Vehicle,AUV)的全覆盖路径规划问题,提出了一种考虑随机初始位置约束的多AUV覆盖路径规划方法(Dividing Areas based on Robots Initial Positions CPP,DARIP-CPP)。根据多自主水下机器人...针对多自主水下航行器(Autonomous Underwater Vehicle,AUV)的全覆盖路径规划问题,提出了一种考虑随机初始位置约束的多AUV覆盖路径规划方法(Dividing Areas based on Robots Initial Positions CPP,DARIP-CPP)。根据多自主水下机器人的随机初始位置对工作海域进行均衡区域划分,将划分所得的不重叠区域分配给多AUV进行独立覆盖路径规划,每台AUV利用生物启发神经网络(Bio-inspired Neural Network)优化各个区域的全覆盖路径。为了克服传统全覆盖路径规划中的“死区”问题,采用A^(*)路径规划算法进行“死区”逃离,沿着较短的路径快速到达未覆盖区域点。仿真结果表明,所提出的DARIPCPP方法可有效提高多AUV全覆盖目标区域的工作效率。展开更多
Emotional bio-robots have become a hot research topic in last two decades. Though there have been some progress in research, design and development of various emotional bio-robots, few of them can be used in practical...Emotional bio-robots have become a hot research topic in last two decades. Though there have been some progress in research, design and development of various emotional bio-robots, few of them can be used in practical applications. The study of emotional bio-robots demands multi-disciplinary co-operation. It involves computer science, artificial intelligence, 3D computation, engineering system modelling, analysis and simulation, bionics engineering, automatic control, image processing and pattern recognition etc. Among them, face detection belongs to image processing and pattern recognition. An emotional robot must have the ability to recognize various objects, particularly, it is very important for a bio-robot to be able to recognize human faces from an image. In this paper, a face detection method is proposed for identifying any human faces in colour images using human skin model and eye detection method. Firstly, this method can be used to detect skin regions from the input colour image after normalizing its luminance. Then, all face candidates are identified using an eye detection method. Comparing with existing algorithms, this method only relies on the colour and geometrical data of human face rather than using training datasets. From experimental results, it is shown that this method is effective and fast and it can be applied to the development of an emotional bio-robot with further improvements of its speed and accuracy.展开更多
基金supported by the Chinese National Natural Science Foundation(Grant No.30970883)the Fundamental Research Funds for the Central Universitiesties(Grant No.CDJRC10230012)chongqing University Innovation Fund(200801A1B0250284)
文摘A system is described here that can noninvasively control the navigation of freely behaving rat via ultrasonic,epidermaland LED photic stimulators on the back.The system receives commands from a remote host computer to deliver specifiedelectrical stimulations to the hearing,pain and visual senses of the rat respectively.The results demonstrate that the three stimuliwork in groups for the rat navigation.We can control the rat to proceed and make right and left turns with great efficiency.Thisexperiment verified that the rat was able to reach a setting destination in the way of cable with the help of a person through theappropriate coordination of the three stimulators.The telemetry video camera mounted on the head of the rat also achieveddistant image acquisition and helped to adjust its navigation path over a distance of 300 m.In a word,the non-invasive motioncontrol navigation system is a good,stable and reliable bio-robot.
文摘针对多自主水下航行器(Autonomous Underwater Vehicle,AUV)的全覆盖路径规划问题,提出了一种考虑随机初始位置约束的多AUV覆盖路径规划方法(Dividing Areas based on Robots Initial Positions CPP,DARIP-CPP)。根据多自主水下机器人的随机初始位置对工作海域进行均衡区域划分,将划分所得的不重叠区域分配给多AUV进行独立覆盖路径规划,每台AUV利用生物启发神经网络(Bio-inspired Neural Network)优化各个区域的全覆盖路径。为了克服传统全覆盖路径规划中的“死区”问题,采用A^(*)路径规划算法进行“死区”逃离,沿着较短的路径快速到达未覆盖区域点。仿真结果表明,所提出的DARIPCPP方法可有效提高多AUV全覆盖目标区域的工作效率。
文摘Emotional bio-robots have become a hot research topic in last two decades. Though there have been some progress in research, design and development of various emotional bio-robots, few of them can be used in practical applications. The study of emotional bio-robots demands multi-disciplinary co-operation. It involves computer science, artificial intelligence, 3D computation, engineering system modelling, analysis and simulation, bionics engineering, automatic control, image processing and pattern recognition etc. Among them, face detection belongs to image processing and pattern recognition. An emotional robot must have the ability to recognize various objects, particularly, it is very important for a bio-robot to be able to recognize human faces from an image. In this paper, a face detection method is proposed for identifying any human faces in colour images using human skin model and eye detection method. Firstly, this method can be used to detect skin regions from the input colour image after normalizing its luminance. Then, all face candidates are identified using an eye detection method. Comparing with existing algorithms, this method only relies on the colour and geometrical data of human face rather than using training datasets. From experimental results, it is shown that this method is effective and fast and it can be applied to the development of an emotional bio-robot with further improvements of its speed and accuracy.
