Mobile Intelligent Terminal Navigation System Research And Application Of Shortest Path Algorithm

With the rise of mobile intelligence platform, as opposed to the traditional navigation equipment information silos, mobile intelligence platform with congenital mobile internet information superiority began to replace the traditional navigation systems. Mobile intelligence platform-based navigation systems can provide users with fast path planning, obtain real-time road information through mobile terminal。So it can reasonably guide the search process and calculate the theoretical optimal rather than the optimal planning path, as well as solving a series of problems of getting lost in the process of car and traffic congestion. The shortest paths planning algorithms are the core technology of the navigation system, but at present most of the navigation systems based on the mobile platforms are still using the traditional static shortest path planning algorithm, which cannot conduct time-varying network path planning and take mobile intelligent network platforms’advantages.In this paper, the classical path planning algorithms, such as Dijkstra algorithm, A-Star algorithm, are studied in details. Furthermore, we also analyzed the improvement and enhancement of the path planning algorithms. With respect to the shortcomings of classical algorithms, we propose a heuristic dynamic path planning algorithm based on landmark-orientation. The algorithm is divided into three parts:data preprocessing module based on landmark, the landmark-based heuristic path querying module, and data updating module based on dynamic shortest path tree. The algorithm introduces a data preprocessing mechanism, which greatly improves the query efficiency of the algorithm and reduces memory space. In order to adapt to the dynamic network, this paper introduces data updating mechanism based on dynamic shortest path tree in the algorithm. When weight changes occur in the network, the algorithm only needs to start the data updating mechanism to update preprocessed data, so that the algorithm can not only ensure the robustness and accuracy of the query but also obtain a better query time. This work has been experimented with comparison of the classical algorithms. The experimental results show that the preprocessing time and query efficiency of the proposed algorithm are successfully achieved. In addition, by simulating the dynamic scenario, we test the proposed algorithm in time-varying network. The experimental results demonstrate the proposed algorithm is suitable for dynamic scenario. In addition, the experiments proved that the algorithm has obtained efficiency, real-time queries, query accuracy, less memory usage, and so on.