Intelligent tracking system based on composite algorithm fusion

In today's social rapid development of science and technology environment, for autonomy and efficiency of artificial intelligence has become very important, in order to promote the future more people for the convenience of artificial intelligence on the tracking, and with the complexity of the application scenario and variability, especially in the dynamic obstacle and unknown environment path tracking, in the autonomous tracking algorithm needs a highly flexible, fast general algorithm. At this time, the memory enhancement path optimization algorithm (memory enhancement path finding algorithm, MEPA) can provide a convenient process and idea. Compared with the traditional tracking algorithm, this algorithm is more adaptable to the environment, and the simulation experiment shows that the algorithm can reach a reuse rate of more than 70% after increasing the complexity of the environment. This greatly reduces the time consumption of repeated pathway calculations.