In recent years, the robotics industry has been a hotspot and provided much convenience in people's daily life. For further research on robots, we introduce the problem of velocity-level minimum joint motion planning and control in this paper. Firstly, by using the Zhang neural network (ZNN) method and the Lagrange multiplier method, a continuous-time ZNN model is presented to solve the problem. Besides, an advanced ten-instant time-discretization formula with higher precision is presented, and five discrete-time ZNN (DTZNN) models are listed because of the digital hardware's requirements. At last, computer and robot experiments verify the effectiveness and feasibility of the presented DTZNN models.

A fast image in painting method based on hybrid similarity-distance is proposed in this paper. In Criminisi et al.'s work, similarity distance are not reliable enough in many cases and the algorithm performs inefficiently. To solve these problems, we propose a new searching strategy to accelerate the algorithm. In addition, we modify the confidence-updating rule to make more reasonable the distributions of the confidences in source region. Besides, taking account of the stationarity of texture and the reliability of the source regions, we present a hybrid similarity-distance, which combines the distance in color space with the distance in spatial space by weight coefficients related to the confidence value. A more reasonable patch will be found out by this hybrid similarity-distance. The experiments verify that the proposed method yields qualitative improvements compared to Criminisi et al.'s work.