It is an effective method to improve the absolute positioning accuracy (APA) of robot’s end-effector by geometric parameters calibration. In this paper, zero reference model (ZRM) and modified Denavit-Hartenberg (MDH) methods are adopted to establish the geometric parameters model of series robot, respectively. Least squares method (LSM) is used to minimize error magnitude in a function modeled over analytical Jacobian of the robot. By carrying out the practical calibration for Staubli Tx60 industrial robot with a Leica 960 laser tracker, the experimental results verify that in robot workspaces the mean absolute positioning errors is reduced from 0.5864 mm before calibration to 0.0737 mm based on ZRM and to 0.1319 mm based on MDH after calibration, respectively. The comparative study shows that ZRM and MDH methods can enhance robot APA and the improvement by ZRM is superior to that by MDH.
The Chang’E-1 Laser Altimeter(LAM), as one of the scientific instruments onboard the Chinese Chang’E-1 orbiter, has successfully gained the massive lunar elevation scientific data of global topography of the moon. Uncertainty evaluation of the lunar elevation detection error based on LAM scientific data is developed in this paper. Firstly, the data are selected from the flat terrain region in all the lunar elevation detection data; Secondly, after the pseudo elevation data are removed in the selected region, regional elevation mean and standard deviation are calculated. Making use of the calculations and taking into account all kinds of uncertainty contributors of LAM orbiting exploring, the uncertainty evaluation methods of the LAM in-orbit elevation exploring are proposed on the basis of the guide to Monte Carlo Methods. Finally, the uncertainty evaluation results of different regions of lunar surface are given. The evaluation results not only can provide the basis for further analysis laser altimeter measurement error sources, but also give the reference for making the high precision moon digital elevation graph and provide theoretical guidance for the accuracy requirement of design of payload on lunar orbiter.
The inverse kinematics control of a robotic manipulator requires solving non-linear equations having transcendental
functions and involving time-consuming calculations. Particle Swarm Optimization (PSO), which is based on the
behaviour of insect swarms and exploits the solution space by taking into account the experience of the single particle as
well as that of the entire swarm, is similar to the genetic algorithm (GA) in that it performs a structured randomized
search of an unknown parameter space by manipulating a population of parameter estimates to converge on a suitable
solution. In this paper, PSO is firstly proposed to optimize feed-forward neural network for manipulator inverse
kinematics. Compared with the results of the fast back propagation learning algorithm (FBP), conventional GA genetic
algorithm based elitist reservation (EGA), improved GA (IGA) and immune evolutionary computation (IEC), the
simulation results verify the particle swarm optimization neural network (PSONN) is effective for manipulator inverse