基于Floyd和改进遗传算法的丘陵地区农田遍历路径规划  

Traversal Path Planning for Farmland in Hilly Areas Based on Floyd and Improved Genetic Algorithm

作　　者：周龙港 刘婷 卢劲竹 ZHOU Longgang;LIU Ting;LU Jinzhu(School of Mechanical Engineering,Xihua University,Chengdu 610039,China;Research Institute of Modern Agricultural Equipment,Xihua University,Chengdu 610039,China)

机构地区：[1]西华大学机械工程学院,四川成都610039 [2]西华大学现代农业装备研究院,四川成都610039

出　　处：《智慧农业（中英文）》2023年第4期45-57,共13页Smart Agriculture

基　　金：四川省科技厅重点研发项目(2021YFN0020);西华大学重点基金项目(Z202132);四川省现代农业装备工程技术研究中心(XDNY2021-004);成都市科技局技术创新研发项目(2022-YF05-01127-SN)。

摘　　要：[目的/意义]本研究针对丘陵地区的农田环境下农业机器人遍历多个田块的遍历路径问题,提出了一种Floyd算法与改进遗传算法相结合的遍历路径规划方法。[方法]首先建立田块间的连通关系以及路网图;然后利用Floyd算法获得任意两个田块间覆盖路径端点距离,再将该距离代价作为变量带入改进遗传算法进行求解,最终得到优化后的田块遍历顺序以及每个田块的进出口分布。[结果和讨论]仿真结果表明,与传统遗传算法相比,本研究提出的改进遗传算法平均最短路径缩短13.8%,算法收敛迭代次数更少,并表现出较好的跳出局部最优解的能力。利用真实的农田数据和田间作业参数进行仿真试验,通过本研究方法得到的田块遍历顺序和进出口的排布能够有效地减少转移路径的长度和路径的重复率。[结论]本研究在农机多田块遍历路径规划上的优越性和可行性,算法输出的轨迹坐标能为农机驾驶员或无人农机在大面积作业时提供路径参考。本研究可为农业机器人遍历路径规划提供技术支持。[Objective]To addresses the problem of traversing multiple fields for agricultural robots in hilly terrain,a traversal path planning method is proposed by combining the Floyd algorithm with an improved genetic algorithm.The method provides a solution that can reduce the cost of agricultural robot operation and optimize the order of field traversal in order to improve the efficiency of farmland operation in hilly areas and realizes to predict how an agricultural robot can transition to the next field after completing its coverage path in the current field.[Methods]In the context of hilly terrain characterized by small and densely distributed field blocks,often separated by field ridges,where there was no clear connectivity between the blocks,a method to establish connectivity between the fields was proposed in the research.This method involved projecting from the corner node of the headland path in the current field to each segment of the headland path in adjacent fields vertically.The shortest projected segment was selected as the candidate connectivity path between the two fields,thus establishing potential connectivity between them.Subsequently,the connectivity was verified,and redundant segments or nodes were removed to further simplify the road network.This method allowed for a more accurate assessment of the actual distances between field blocks,thereby providing a more precise and feasible distance cost between field blocks for multi-block traversal sequence planning.Next,the classical graph algorithm,Floyd algorithm,was employed to address the shortest path problem for all pairs of nodes among the fields.The resulting shortest path matrix among headland path nodes within fields,obtained through the Floyd algorithm,allowed to determine the shortest paths and distances between any two endpoint nodes in different fields.This information was used to ascertain the actual distance cost required for agricultural machinery to transfer between fields.Furthermore,for the genetic algorithm in path planning,there were

关 键 词：丘陵地区 农业机器人 遍历路径规划 FLOYD算法 改进遗传算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] S277[自动化与计算机技术—控制科学与工程]