基于IWHO-EKF的高速免耕播种机播种深度监测系统研究OA北大核心CSTPCD
High-speed No-till Seeder Seeding Depth Monitoring System Based on IWHO-EKF
为解决免耕播种机高速(12~16 km/h)作业时因地势起伏造成机械振动与传感器测量误差导致的播种深度监测系统精度降低,以及单一传感器监测可靠性较差的问题,研究了一种基于改进野马算法(Improved wild horse optimizer,IWHO)优化扩展卡尔曼滤波器(Extended Kalman filter,EKF)中关键参数Qsigma、Rsigma1、Rsgma2、Rsigma3的多传感器数据融合算法(IWHO-EKF)的高速免耕播种机播种深度监测系统.首先,建立以激光、超声波与角度传感器为多传感器监测单元的播种深度监测模型;其次,通过卡尔曼滤波算法对3个单一传感器分别滤波;最后,提出一种加入莱维飞行与高斯变异的IWHO-EKF算法,将滤波后的3个单一传感器进行数据融合,从而解决机械振动干扰与传感器测量误差降低的问题,同时充分发挥多传感器融合信息,确保免耕播种机高速作业时实现高精度、高可靠性播种深度实时监测.为验证其优越性,通过IWHO-EKF算法与单一传感器监测、单一传感器滤波和WHO-EKF算法进行仿真对比试验与田间试验.仿真试验表明:基于IWHO-EKF的高速免耕播种机播种深度监测算法平均绝对误差为0.073 cm,均方根误差为0.090 cm,相关系数为0.983,实现了高精度监测,且精度相较于传感器原始监测值、滤波值与WHO-EKF算法均显著提升.田间试验结果表明:基于IWHO-EKF算法的高速免耕播种机播种深度监测系统相较于3个单一传感器监测值,平均绝对误差和平均均方根误差分别降低0.063 cm和0.067 cm,同时平均相关系数提升0.027,该系统能够提高播种深度监测系统的精确性和可靠性.
A high-speed no-tillage seeder seeding depth monitoring system based on the improved wild horse optimizer-extended Kalman filter(IWHO-EKF)was proposed.The system addressed the mechanical vibration issues caused by uneven terrain during operation,which led to a decrease in accuracy of the seeding depth monitoring.Additionally,it improved the poor reliability of a single monitoring sensor.Firstly,a mathematical model for monitoring seeding depth was established by using laser,ultrasonic,and angle sensors as the multi-sensor monitoring unit.Secondly,a Kalman filtering algorithm was implemented to filter the measurements from the three individual sensors separately.Lastly,the IWHO proposed the use of the Levy flight and Gaussian mutation algorithms to optimize the key parameters of the EKF for data fusion.Qsigma,Rsigma1,Rsigma2,and Rsigma3 were the parameters that were optimized for the fusion of filtered measurements from the three sensors.Technical term abbreviations such as EKF were explained when first used.The aim was to reduce interference from mechanical vibration,decrease sensor measurement errors and ensure accurate and reliable real-time seeding depth monitoring during high-speed operation of the no-till seeder.To ascertain the effectiveness of the proposed method,simulation experiments and field validation experiments were conducted,comparing the IWHO-EKF with original sensor measurements,filtered seeding depth values and the WHO-EKF.The results from simulation experiments demonstrated that the IWHO-EKF algorithm had a mean absolute error(MAE)and root mean squared error(RMSE)of 0.073 cm and 0.090 cm,respectively,with a high correlation coefficient(R)of 0.983.This suggested a high level of accuracy and significant improvements in precision compared with measurements from the original sensor and filtered values,as well as the WHO-EKF.Technical term abbreviations were explained when it was firstly used.Field validation tests confirmed that the IWHO-EKF for seeding depth monitoring system in high-speed no-till seeders reduced the average MAE and RMSE by 0.063 cm and 0.067 cm,respectively,when compared with data from the three sensors.Additionally,the average R was increased by 0.027.This system offerred improved,accurate,and dependable monitoring values for seeding depth.The research result can provide lessons and references for high precision seeding depth monitoring during high-speed seeding.
王淞;衣淑娟;赵斌;李衣菲;陶桂香;毛欣
黑龙江八一农垦大学工程学院,大庆 163319黑龙江八一农垦大学工程学院,大庆 163319||黑龙江省农机智能装备重点实验室,大庆 163319黑龙江八一农垦大学工程学院,大庆 163319||东北农业大学工程学院,哈尔滨 150030
农业工程
高速免耕播种机播种深度监测系统改进野马算法扩展卡尔曼滤波器数据融合
high-speed no-till seederseeding depth monitoringimproved wild horse optimizerextended Kalman filterdata fusion
《农业机械学报》 2024 (003)
75-84 / 10
国家自然科学基金项目(52275246)、黑龙江省重点研发计划重大项目(2022ZX05B02)和黑龙江省"百千万"工程科技重大专项(2020ZX17B01-3)
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