Search

In order to solve the problems of deadlock, slow convergence, easy to get into local optimum and uneven path when ant colony algorithm is applied to the path planning of mobile robots, a smooth path planning method combining improved A* ant colony algorithm and the rolling window method is proposed. Firstly, the improved A* algorithm is used to initialize the ant colony pheromone to solve the problem of low ant colony efficiency. Then, the state transition probability function is improved to consider the feasible path “activity” and the end position in the function to avoid deadlock phenomenon. At the same time, based on the mechanism of inequality principle, the pheromone of ant colony is updated to avoid falling into the local optimal path and accelerate the convergence speed of the algorithm. Secondly, based on the global path planning, local real-time path planning is carried out by integ-rating the rolling window method and the dynamic obstacle avoidance strategy. Finally, Bessel curve is used to process the smoothness of the planned path, so that the smoothed path is closer to the actual motion path. In order to ensure the best performance of the algorithm, genetic algorithm with elite strategy is used to optimize the parameters of the algorithm. Three sets of experimental results show that the proposed algorithm is effective in the presence of simple or complex, static or dynamic obstacles.

With the development of the Internet, online recruitment has gradually become the mainstream. Analyzing the recruitment data and exploring the internal rules of the data can help enterprises and job seekers realize human-career matching. An extremely important step in analyzing recruitment data is to extract structured information from unstructured data. Named entity recognition can effectively extract entity information from unstructured data. In recent years, a lot of work has focused on this task, but no related work has been applied named entity recognition to Internet recruitment information. Therefore, this paper proposes the BiLSTM-Attention-CRF model for Internet recruitment information, which can be used to extract skill entities in job description information. This model introduces the BiLSTM and Attention mechanism to improve the effect of entity recognition. To verify the performance of the model, the paper used crawler technology to capture the real Internet recruitment data and annotate it and obtain a real data set. In this paper, a series of experiments are conducted on this data set, and the experimental results show that the proposed model achieves the best performance.