An autonomous navigation method for orchard rows based on a combination of an improved a-star algorithm and SVR

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2024-02-20 DOI:10.1007/s11119-024-10118-z

Minghui Wang, Jian Xu, Jin Zhang, Yongjie Cui

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Abstract

Autonomous robot-based orchard operations will become an alternative solution in the field of precision agriculture. One of the keys to robotic work is to achieve autonomous navigation that is as accurate as possible to ensure the most accurate working effect. In this work, we propose an orchard path fitting and navigation method based on the fusion of improved A-Star algorithm and Support Vector Machine Regression (SVR) to meet the requirements of autonomous navigation crawler platform for autonomous navigation in orchard environment and ensure accuracy. In this study, the actual speed and turning radius of the left and right tracks of the crawler platform were collected under 5 different slopes and 400 sets of different theoretical speed combinations of left and right tracks through the design nesting test, and the motion model of the crawler platform was constructed based on SVR. Orchard point cloud data were obtained by 3D solid-state LiDAR, and the improved A-star algorithm was used to fit the navigation path and calculate the turning curvature radius. Taking this curvature radius as the optimal navigation target value, the motion model predicts the optimal theoretical speed of left and right tracks, which is used as a reference for autonomous navigation. The comparison experiment of autonomous navigation was carried out in two modes: traditional and improved A-Star algorithm. The results show that the average values of the maximum lateral and longitudinal deviation of the improved automatic navigation method between orchards row are 6.90 cm and 9.88 cm, respectively. Compared with the method combined with the traditional A-Star algorithm and SVR, the values were 8.94 cm and 10.88 cm and were optimized by 29.57% and 10.12%, respectively. The autonomous navigation method proposed in this paper can meet the needs of orchards rows autonomous navigation, and can be widely applied to different orchard site environments (slope, ground obstacles, bad surface conditions), which can provide reference for the production practices of unmanned orchards.

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基于改进型星形算法和 SVR 组合的果园行列自主导航方法

基于机器人的果园自主作业将成为精准农业领域的另一种解决方案。机器人作业的关键之一是实现尽可能精确的自主导航，以确保最精确的作业效果。在这项工作中，我们提出了一种基于改进型 A-Star 算法和支持向量机回归（SVR）融合的果园路径拟合与导航方法，以满足自主导航爬虫平台在果园环境中自主导航的要求，并确保精度。本研究通过设计嵌套试验，采集了爬行平台在 5 个不同坡度和 400 组左右履带不同理论速度组合下的左右履带实际速度和转弯半径，并基于 SVR 构建了爬行平台的运动模型。Orchard 点云数据由三维固态激光雷达获取，采用改进的 A-star 算法拟合导航路径并计算转弯曲率半径。以该曲率半径为最佳导航目标值，运动模型预测出左右轨迹的最佳理论速度，作为自主导航的参考。自主导航的对比实验分为两种模式：传统的 A-Star 算法和改进的 A-Star 算法。结果表明，改进后的自动导航方法在果园行间的最大横向偏差和纵向偏差的平均值分别为 6.90 厘米和 9.88 厘米。与结合传统 A-Star 算法和 SVR 的方法相比，其数值分别为 8.94 厘米和 10.88 厘米，分别优化了 29.57% 和 10.12%。本文提出的自主导航方法能满足果园行间自主导航的需要，可广泛应用于不同的果园现场环境（坡度、地面障碍物、不良地表条件），可为无人果园的生产实践提供参考。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.