The global map cannot be accurately known in the path planning of mobile robots. In order to solve the problem, a local path planning algorithm based on fuzzy rules and artificial potential field method was proposed. Firstly, the ranging group and fuzzy rules were used to classify the shape of obstacles and construct the local maps. Secondly, a modified repulsive force function was introduced in the artificial potential field method. Based on the local maps, the local path planning was performed by using the artificial potential field method. Finally, with the movement of robot, time breakpoints were set to reduce path oscillation. For the maps of random obstacles and bumpy obstacles, the traditional artificial potential field method and the improved artificial potential field method were respectively used for simulation. The experimental results show that, in the case of random obstacles, compared with the traditional artificial potential field method, the improved artificial potential field method can significantly reduce the collision of obstacles; in the case of bumpy obstacles, the improved artificial potential field method can successfully complete the goal of path planning. The proposed algorithm is adaptable to terrain changes, and can realize the path planning of six-legged robot under unknown maps.

The existing link prediction methods based on node similarity usually ignore the link strength of network topology and the weight value in the typological path method with weight is difficult to set. To solve these problems, a new prediction algorithm based on link importance and data field was proposed. Firstly, this method assigned different weight for each link according to the topology graph. Secondly, it took into account the interaction between potential link nodes and pre-estimated the link values for the partial nodes without links. Finally, it calculated the similarity between two nodes with data field potential function. The experimental results on some typical data sets of the real-world network show that, the proposed method has good performance with both classification index and recommended index. In comparison to the Local Path (LP) algorithm with the same complexity, the proposed algorithm raises Area Under Curve (AUC) by 3 to 6 percentages, and raises Discounted Cumulative Gain (DCG) by 1.5 to 2.5 points. On the whole, it improves the prediction accuracy. Because of its easy parameter determination and low time complexity, this new approach can be deployed simply.