This paper deals with the scanner exterior calibration algorithm when the scanner is arranged by the robot and the object scanned is fixed on a rotate device in the Robot Remanufacturing System. The method of calibrat...This paper deals with the scanner exterior calibration algorithm when the scanner is arranged by the robot and the object scanned is fixed on a rotate device in the Robot Remanufacturing System. The method of calibrating the relationship between the scanner coordinate and the robot Tool0, such as the rotation, Rx, Ry, Rz, and the transformation ,Y, Z is studied. The data of Tool0 can be directly obtained from the relationship with the robot base-coordinate. So, the coordinate relationship between the scanner coordinate and the robot base coordinate can be easily gotten. This paper explains the basic algorithm theory, computing method, data collecting process and the resulted data in detail. The calibration algorithm is deduced under the orthogonal coordinate.展开更多
The multi-objective genetic algorithm(MOGA) is proposed to calibrate the non-linear camera model of a space manipulator to improve its locational accuracy. This algorithm can optimize the camera model by dynamic balan...The multi-objective genetic algorithm(MOGA) is proposed to calibrate the non-linear camera model of a space manipulator to improve its locational accuracy. This algorithm can optimize the camera model by dynamic balancing its model weight and multi-parametric distributions to the required accuracy. A novel measuring instrument of space manipulator is designed to orbital simulative motion and locational accuracy test. The camera system of space manipulator, calibrated by MOGA algorithm, is used to locational accuracy test in this measuring instrument. The experimental result shows that the absolute errors are [0.07, 1.75] mm for MOGA calibrating model, [2.88, 5.95] mm for MN method, and [1.19, 4.83] mm for LM method. Besides, the composite errors both of LM method and MN method are approximately seven times higher that of MOGA calibrating model. It is suggested that the MOGA calibrating model is superior both to LM method and MN method.展开更多
Local invariant algorithm applied in downward-looking image registration,usually computes the camera's pose relative to visual landmarks.Generally,there are three requirements in the process of image registration ...Local invariant algorithm applied in downward-looking image registration,usually computes the camera's pose relative to visual landmarks.Generally,there are three requirements in the process of image registration when using these approaches.First,the algorithm is apt to be influenced by illumination.Second,algorithm should have less computational complexity.Third,the depth information of images needs to be estimated without other sensors.This paper investigates a famous local invariant feature named speeded up robust feature(SURF),and proposes a highspeed and robust image registration and localization algorithm based on it.With supports from feature tracking and pose estimation methods,the proposed algorithm can compute camera poses under different conditions of scale,viewpoint and rotation so as to precisely localize object's position.At last,the study makes registration experiment by scale invariant feature transform(SIFT),SURF and the proposed algorithm,and designs a method to evaluate their performances.Furthermore,this study makes object retrieval test on remote sensing video.For there is big deformation on remote sensing frames,the registration algorithm absorbs the Kanade-Lucas-Tomasi(KLT)3-D coplanar calibration feature tracker methods,which can localize interesting targets precisely and efficiently.The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping(vSLAM)in a period of time.展开更多
针对双向快速随机扩展树(rapidly-exploring random trees-connect,RRT-Connect)算法的随机性强、搜索效率低、路径规划时间过长等问题,提出一种改进的RRT-Connect算法。该算法在起始点与目标点连线的中垂线上设置第三节点,采用高斯分...针对双向快速随机扩展树(rapidly-exploring random trees-connect,RRT-Connect)算法的随机性强、搜索效率低、路径规划时间过长等问题,提出一种改进的RRT-Connect算法。该算法在起始点与目标点连线的中垂线上设置第三节点,采用高斯分布限制第三节点的采样区域,避免第三采样节点距离中点较远导致的路径冗余。算法通过第三节点分别向起始点和目标点生成2棵随机树,结合贪婪算法思想以及引入动态步长的方法,提高算法的规划效率。仿真结果表明,改进的RRT-Connect算法相较于传统RRT-Connect算法,平均运行时间缩短了48.7%,平均迭代次数减少了38.9%,平均路径长度减少了25.2%。另外,针对传统的九点标定法精度的问题,提出一种改进的九点标定方法,该方法通过获取机械臂在空间同一点的多组位姿计算机械臂第六轴长度,在已知机械臂各关节角和轴长情况下,计算得到机械臂末端执行器安装后第六轴的长度,从而提高手眼标定的精度。试验结果表明,改进的方法相较于传统九点标定法其精度平均提高了2.09%。最后,在机械臂平台验证改进的RRT-Connect算法和改进的九点标定法,试验结果表明,改进的RRT-Connect算法相较于DRRT-Connect(dynamicRRT-Connect)算法在路径规划总时间和总长度上分别减少了8.28%和4.79%,改进的九点标定法相较于传统的九点标定法抓取精度提高了3%。展开更多
文摘This paper deals with the scanner exterior calibration algorithm when the scanner is arranged by the robot and the object scanned is fixed on a rotate device in the Robot Remanufacturing System. The method of calibrating the relationship between the scanner coordinate and the robot Tool0, such as the rotation, Rx, Ry, Rz, and the transformation ,Y, Z is studied. The data of Tool0 can be directly obtained from the relationship with the robot base-coordinate. So, the coordinate relationship between the scanner coordinate and the robot base coordinate can be easily gotten. This paper explains the basic algorithm theory, computing method, data collecting process and the resulted data in detail. The calibration algorithm is deduced under the orthogonal coordinate.
基金Project(J132012C001)supported by Technological Foundation of ChinaProject(2011YQ04013606)supported by National Major Scientific Instrument & Equipment Developing Projects,China
文摘The multi-objective genetic algorithm(MOGA) is proposed to calibrate the non-linear camera model of a space manipulator to improve its locational accuracy. This algorithm can optimize the camera model by dynamic balancing its model weight and multi-parametric distributions to the required accuracy. A novel measuring instrument of space manipulator is designed to orbital simulative motion and locational accuracy test. The camera system of space manipulator, calibrated by MOGA algorithm, is used to locational accuracy test in this measuring instrument. The experimental result shows that the absolute errors are [0.07, 1.75] mm for MOGA calibrating model, [2.88, 5.95] mm for MN method, and [1.19, 4.83] mm for LM method. Besides, the composite errors both of LM method and MN method are approximately seven times higher that of MOGA calibrating model. It is suggested that the MOGA calibrating model is superior both to LM method and MN method.
基金supported by the National Natural Science Foundation of China(60802043)the National Basic Research Program of China(973 Program)(2010CB327900)
文摘Local invariant algorithm applied in downward-looking image registration,usually computes the camera's pose relative to visual landmarks.Generally,there are three requirements in the process of image registration when using these approaches.First,the algorithm is apt to be influenced by illumination.Second,algorithm should have less computational complexity.Third,the depth information of images needs to be estimated without other sensors.This paper investigates a famous local invariant feature named speeded up robust feature(SURF),and proposes a highspeed and robust image registration and localization algorithm based on it.With supports from feature tracking and pose estimation methods,the proposed algorithm can compute camera poses under different conditions of scale,viewpoint and rotation so as to precisely localize object's position.At last,the study makes registration experiment by scale invariant feature transform(SIFT),SURF and the proposed algorithm,and designs a method to evaluate their performances.Furthermore,this study makes object retrieval test on remote sensing video.For there is big deformation on remote sensing frames,the registration algorithm absorbs the Kanade-Lucas-Tomasi(KLT)3-D coplanar calibration feature tracker methods,which can localize interesting targets precisely and efficiently.The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping(vSLAM)in a period of time.
文摘针对双向快速随机扩展树(rapidly-exploring random trees-connect,RRT-Connect)算法的随机性强、搜索效率低、路径规划时间过长等问题,提出一种改进的RRT-Connect算法。该算法在起始点与目标点连线的中垂线上设置第三节点,采用高斯分布限制第三节点的采样区域,避免第三采样节点距离中点较远导致的路径冗余。算法通过第三节点分别向起始点和目标点生成2棵随机树,结合贪婪算法思想以及引入动态步长的方法,提高算法的规划效率。仿真结果表明,改进的RRT-Connect算法相较于传统RRT-Connect算法,平均运行时间缩短了48.7%,平均迭代次数减少了38.9%,平均路径长度减少了25.2%。另外,针对传统的九点标定法精度的问题,提出一种改进的九点标定方法,该方法通过获取机械臂在空间同一点的多组位姿计算机械臂第六轴长度,在已知机械臂各关节角和轴长情况下,计算得到机械臂末端执行器安装后第六轴的长度,从而提高手眼标定的精度。试验结果表明,改进的方法相较于传统九点标定法其精度平均提高了2.09%。最后,在机械臂平台验证改进的RRT-Connect算法和改进的九点标定法,试验结果表明,改进的RRT-Connect算法相较于DRRT-Connect(dynamicRRT-Connect)算法在路径规划总时间和总长度上分别减少了8.28%和4.79%,改进的九点标定法相较于传统的九点标定法抓取精度提高了3%。
