Three-dimensional continuous picking path planning based on ant colony optimization algorithm

• Published in: PloS one 2023-02, Vol.18 (2), p.e0282334-e0282334
• Main Authors: Zhang, Chuang, Wang, He, Fu, Li-Hua, Pei, Yue-Han, Lan, Chun-Yang, Hou, Hong-Yu, Song, Hua
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Agriculture; Algorithms; Analysis; Ant colony optimization; Ants; Artificial Intelligence; Biology and Life Sciences; Computer and Information Sciences; Convergence; Deep learning; Design; Efficiency; Engineering and Technology; Environmental aspects; Fruit; Fruits; Genetic algorithms; Health aspects; Humans; Mathematical optimization; Modernization; Neural networks; Observations; Optimization; Optimization algorithms; Parameters; Path planning; Pheromones; Physical Sciences; Picking; Planning; Punishment; Research and Analysis Methods; Robots; Success; Swarm intelligence; Traveling salesman problem
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0282334

Fruit-picking robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology, people are demanding higher picking efficiency from fruit-picking robots. And a good fruit-picking path determines the efficiency of fruit-picking. Currently, most picking path planning is a point-to-point approach, which means that the path needs to be re-planned after each completed path planning. If the picking path planning method of the fruit-picking robot is changed from a point-to-point approach to a continuous picking method, it will significantly improve its picking efficiency. The optimal sequential ant colony optimization algorithm(OSACO) is proposed for the path planning problem of continuous fruit-picking. The algorithm adopts a new pheromone update method. It introduces a reward and punishment mechanism and a pheromone volatility factor adaptive adjustment mechanism to ensure the global search capability of the algorithm, while solving the premature and local convergence problems in the solution process. And the multi-variable bit adaptive genetic algorithm is used to optimize its initial parameters so that the parameter selection does not depend on empirical and the combination of parameters can be intelligently adjusted according to different scales, thus bringing out the best performance of the ant colony algorithm. The results show that OSACO algorithms have better global search capability, higher quality of convergence to the optimal solution, shorter generated path lengths, and greater robustness than other variants of the ant colony algorithm.