School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan, China.
PLoS One. 2023 Feb 27;18(2):e0282334. doi: 10.1371/journal.pone.0282334. eCollection 2023.
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.
采摘机器人是促进农业现代化和提高农业效率的重要手段之一。随着人工智能技术的发展，人们对采摘机器人的采摘效率提出了更高的要求。而一条好的采摘路径决定了采摘的效率。目前，大多数采摘路径规划都是点对点的方法，这意味着在每次完成路径规划后都需要重新规划路径。如果将采摘机器人的采摘路径规划方法从点对点方法改为连续采摘方法，将显著提高其采摘效率。针对连续采摘的路径规划问题，提出了最优序列蚁群优化算法（OSACO）。该算法采用了新的信息素更新方法，引入了奖惩机制和信息素挥发因子自适应调整机制，以保证算法的全局搜索能力，同时解决了解过程中的早熟和局部收敛问题。并采用多变量位自适应遗传算法对其初始参数进行优化，使得参数选择不依赖经验，并且可以根据不同的规模智能地调整参数组合，从而发挥蚁群算法的最佳性能。结果表明，OSACO 算法具有更好的全局搜索能力、更高的最优解收敛质量、更短的生成路径长度和更强的鲁棒性，优于蚁群算法的其他变体。
