Humanoid strategies of avoiding obstacles were analyzed and formulated into virtual force field (VFF) algorithm. In view of obstacles moving, smoothing accumulation computing method was designed for updating credibility factors (CF) of grid held by obstacle, and two examples were given. Uncertainty reasoning was adopted for computing complex CF with uncertainty of sensors signal, and synthesize method was designed. The weight of repulsive force was designed to be varying with angle from robot moving direction to obstacles, in order to make robot more sensitivity to obstacles on moving way. Indoor and outdoor virtual environment were established and simulations were carried out. The results demonstrate that the path is better using proposed than common VFF.
Image jitters occur in the video of the autonomous robot moving on bricks road, which will reduce robot operation precision based on vision. In order to compensate the image jitters, the affine transformation kinematics were established for obtaining the six image motion parameters. The feature point pair detecting method was designed based on Eigen-value of the feature windows gradient matrix, and the motion parameters equation was solved using the least square method and the matching point pairs got based on the optical flow. The condition number of coefficient matrix was proposed to quantificationally analyse the effect of matching errors on parameters solving errors. Kalman filter was adopted to smooth image motion parameters. Computing cases show that more point pairs are beneficial for getting more precise motion parameters. The integrated jitters compensation software was developed with feature points detecting in subwindow. And practical experiments were conducted on two mobile robots. Results show that the compensation costing time is less than frame sample time and Kalman filter is valid for robot vision jitters compensation.
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