The paper presents a fuzzy Q-learning(FQL)and optical flow-based autonomous navigation approach.The FQL method takes decisions in an unknown environment and without mapping,using motion information and through a reinf...The paper presents a fuzzy Q-learning(FQL)and optical flow-based autonomous navigation approach.The FQL method takes decisions in an unknown environment and without mapping,using motion information and through a reinforcement signal into an evolutionary algorithm.The reinforcement signal is calculated by estimating the optical flow densities in areas of the camera to determine whether they are“dense”or“thin”which has a relationship with the proximity of objects.The results obtained show that the present approach improves the rate of learning compared with a method with a simple reward system and without the evolutionary component.The proposed system was implemented in a virtual robotics system using the CoppeliaSim software and in communication with Python.展开更多
A system for mobile robot localization and navigation was presented.With the proposed system,the robot can be located and navigated by a single landmark in a single image.And the navigation mode may be following-track...A system for mobile robot localization and navigation was presented.With the proposed system,the robot can be located and navigated by a single landmark in a single image.And the navigation mode may be following-track,teaching and playback,or programming.The basic idea is that the system computes the differences between the expected and the recognized position at each time and then controls the robot in a direction to reduce those differences.To minimize the robot sensor equipment,only one omnidirectional camera was used.Experiments in disturbing environments show that the presented algorithm is robust and easy to implement,without camera rectification.The rootmean-square error(RMSE) of localization is 1.4,cm,and the navigation error in teaching and playback is within 10,cm.展开更多
以非合作航天器的相对状态确定为研究背景,针对无法在目标航天器上安装测量光标的问题,提出利用目标航天器自然特征的单目视觉测量方案.并针对仅利用非合作航天器自然特征而导致的粗大误差增大等问题,提出了基于Randomized RANdom SAmpl...以非合作航天器的相对状态确定为研究背景,针对无法在目标航天器上安装测量光标的问题,提出利用目标航天器自然特征的单目视觉测量方案.并针对仅利用非合作航天器自然特征而导致的粗大误差增大等问题,提出了基于Randomized RANdom SAmple Consensus(R-RANSAC)的相对位姿单目视觉确定鲁棒算法,该算法首先采用R-RANSAC剔除粗大误差,然后利用基于特征点的相对位姿确定迭代算法消除其他类型的误差影响,以进一步提高算法确定精度.与航天器交会对接视觉系统不同,该系统无需在目标航天器上安装测量光标,而是充分利用目标航天器的自身结构特征,因此更适用于非合作航天器间的相对状态测量.最后对本文算法进行了数学仿真,结果表明该算法的有效性和可靠性.展开更多
文摘The paper presents a fuzzy Q-learning(FQL)and optical flow-based autonomous navigation approach.The FQL method takes decisions in an unknown environment and without mapping,using motion information and through a reinforcement signal into an evolutionary algorithm.The reinforcement signal is calculated by estimating the optical flow densities in areas of the camera to determine whether they are“dense”or“thin”which has a relationship with the proximity of objects.The results obtained show that the present approach improves the rate of learning compared with a method with a simple reward system and without the evolutionary component.The proposed system was implemented in a virtual robotics system using the CoppeliaSim software and in communication with Python.
基金Supported by National Natural Science Foundation of China (No. 31000422 and No. 61201081)Tianjin Municipal Education Commission(No.20110829)Tianjin Science and Technology Committee(No. 10JCZDJC22800)
文摘A system for mobile robot localization and navigation was presented.With the proposed system,the robot can be located and navigated by a single landmark in a single image.And the navigation mode may be following-track,teaching and playback,or programming.The basic idea is that the system computes the differences between the expected and the recognized position at each time and then controls the robot in a direction to reduce those differences.To minimize the robot sensor equipment,only one omnidirectional camera was used.Experiments in disturbing environments show that the presented algorithm is robust and easy to implement,without camera rectification.The rootmean-square error(RMSE) of localization is 1.4,cm,and the navigation error in teaching and playback is within 10,cm.
文摘以非合作航天器的相对状态确定为研究背景,针对无法在目标航天器上安装测量光标的问题,提出利用目标航天器自然特征的单目视觉测量方案.并针对仅利用非合作航天器自然特征而导致的粗大误差增大等问题,提出了基于Randomized RANdom SAmple Consensus(R-RANSAC)的相对位姿单目视觉确定鲁棒算法,该算法首先采用R-RANSAC剔除粗大误差,然后利用基于特征点的相对位姿确定迭代算法消除其他类型的误差影响,以进一步提高算法确定精度.与航天器交会对接视觉系统不同,该系统无需在目标航天器上安装测量光标,而是充分利用目标航天器的自身结构特征,因此更适用于非合作航天器间的相对状态测量.最后对本文算法进行了数学仿真,结果表明该算法的有效性和可靠性.
