Picking robot path planning based on improved ant colony algorithm

Yuke Liu, Qingyong Zhang, Lijuan Yu
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引用次数: 1

Abstract

With the increasing progress of agricultural production systems, the requirements for the degree of automation are also growing. As the most arduous picking link in the whole agricultural production, the development of picking robots has been paid more and more attention by experts and scholars. How to make the picking robot adapt to the complex and diverse agricultural production environment and effectively replace the manual operation, it is necessary to plan the path of the picking robot carefully. In this paper, a path planning method based on ant colony algorithm is proposed. The basic ant colony algorithm has the shortcomings of slow convergence speed and easy to fall into local optimum so that the final algorithm cannot meet the needs of the target. Firstly, the grid method is used to simulate the agricultural production environment to improve the applicability of path planning. Secondly, a new pheromone initialization scheme is proposed to improve the convergence speed because of the absence of pheromone in the initial time of the basic ant colony algorithm. Then, aiming at the problem that the basic ant colony algorithm is easy to fall into an optimal local solution, a new pheromone initialization scheme is proposed. The pheromone is added or subtracted to make the ant colony converge to the optimal path better. Finally, the performance of the algorithm is improved by choosing the appropriate heuristic function. Simulation experiments show that the path planning based on the improved ant colony algorithm has a good effect on the path planning process of the picking robot.
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基于改进蚁群算法的拾取机器人路径规划
随着农业生产系统的不断进步,对自动化程度的要求也越来越高。作为整个农业生产中最繁重的采摘环节,采摘机器人的发展越来越受到专家学者的重视。如何使采摘机器人适应复杂多样的农业生产环境,有效地取代人工操作,需要对采摘机器人的路径进行精心规划。提出了一种基于蚁群算法的路径规划方法。基本蚁群算法存在收敛速度慢、容易陷入局部最优的缺点,使得最终算法不能满足目标的需要。首先,采用网格法对农业生产环境进行模拟,提高路径规划的适用性;其次,针对基本蚁群算法初始时间不存在信息素的问题,提出了一种新的信息素初始化方案,提高了算法的收敛速度;然后,针对基本蚁群算法容易陷入局部最优解的问题,提出了一种新的信息素初始化方案。通过增加或减少信息素,使蚁群更好地收敛到最优路径。最后,通过选择合适的启发式函数来提高算法的性能。仿真实验表明,基于改进蚁群算法的路径规划对拾取机器人的路径规划过程具有良好的效果。
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