Research on Tea Harvesting Path Planning Based on Improved Ant Colony Algorithm

This article proposes a tea harvesting robot path planning method based on an improved ant colony algorithm. Firstly, it analyzes the characteristics of tea harvesting robot path planning, and introduces the basic principles and model of the ant colony algorithm. Then, in order to address the issue of the ant colony algorithm easily getting stuck in local optima, it combines the principles of genetic algorithms to incorporate operations such as swapping, insertion, and reversal into the ant colony algorithm. Experimental results demonstrate that the improved ant colony algorithm exhibits better path planning efficiency and convergence speed as the number of tea leaf buds increases. Compared to the classical ant colony algorithm, it effectively avoids the problem of getting trapped in local optima.