Research on path planning of tea picking robot based on ant colony algorithm

• Published in: Measurement and control (London) Vol. 57; no. 8; pp. 1051 - 1067
• Main Authors: Wu, Minghui, Gao, Bo, Hu, Heping, Hong, Konglin
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.08.2024; Sage Publications Ltd; SAGE Publishing
• Subjects: Adaptive algorithms; Algorithms; Ant colony optimization; Basic converters; Convergence; Pheromones; Picking; Robots; Search algorithms; Shortest path planning; Vaporization; Visual aspects; Tea picking robot; path planning; ant colony algorithm; path optimization
• ISSN: 0020-2940; 2051-8730
• DOI: 10.1177/00202940241228996

Robot tea picking is an inevitable trend to solve the problem of tea picking, and the picking path planning is directly related to the robot picking efficiency. An Improved Ant Colony Algorithm (IACA) is proposed, which firstly introduces the adaptive adjustment mechanism into the pheromone volatilization factor of the ant colony algorithm, and then sets the pheromone volatilization factor with a high initial value to improve the searching speed, and then adjusts the size of its value within a certain range in real time according to the iterative results, and finally solves the problem that the searching of the ant colony algorithm is prone to fall into the local optimal solution. On the basis of visual recognition of tea leaves and obtaining coordinate information, the improved ant colony algorithm is used to enter the path planning simulation experiments, and the planning results of the other six algorithms are compared with the similar algorithms and dissimilar algorithms, and the experimental results indicate that the IACA method has improved the shortest path index by 5% compared to the basic ant colony algorithm, and by an average of 4% compared to similar improved ant colony algorithms. In comparison to different optimization algorithms, the enhancement has an average increase of 6%; Furthermore, the convergence speed has been improved by 60% compared to six other methods. The standard deviation of repeated experimental results is 50% lower than the other six methods. The gap between the results of multiple repeated experiments is small, the degree of fluctuation is low, and the calculation results are more stable, which verifies the superiority of IACA method. Therefore, the improvement of the ant colony algorithm makes the pheromone concentration value with adaptive adjustment ability, which reflects good effects in path optimization, convergence speed improvement, stability of results, etc., and has good application value for the path planning problems such as tea picking, which has complex paths and large computational volume.