基于北斗RTK的小区精量播种机区间控制算法研究OA

Plot seeding is a critical step in crop breeding experiments,demanding extremely high operational precision.It requires not only precise control of the alternating sections between plots and spacing lanes during operation but also ensuring that the actual seed landing points align with the theoretical spatial boundaries to achieve accurate plot length measurement.To address the shortcomings of traditional methods in boundary identification,length control,and system delay compensation,this paper proposed a collaborative control algorithm integrating centimeter-level BeiDou RTK posi-tioning,projection accumulation-based dynamic state switching,and landing point prediction.Firstly,a projection accu-mulation and dynamic state switching trigger algorithm was proposed.By constructing a dual-state machine model(see-ding-spacing lane)and employing a distance accumulation method based on along-track projection along with a dynamic state judgment mechanism,it achieved precise boundary identification and mode switching between plots and spacing lanes.Secondly,a kinematics-based feedforward landing point prediction model was established,creating a mapping re-lationship between the seed landing lag distance and real-time velocity.This enabled the advance triggering of seeding actions,compensating for seed landing deviations and ensuring the authenticity of plot and spacing lane length measure-ments based on actual seeding points.Field experiment results demonstrate:In tracer marker comparison tests,the pro-jection accumulation algorithm achieved the cumulative error of only 3.5 cm over 100 m,an 81.2%improvement over the traditional Euclidean distance accumulation algorithm.In state switching tests within the speed range of 0.5-3.0 km/h,the mean absolute deviation of plot length was 1.8-3.2 cm,with 100%switching success rate.In landing point prediction tests,the average deviation between the first seed's landing point and the theoretical starting point was 3.3 cm,with a standard deviation of 2.5 cm.This study provides a comprehensive technical solution for achieving high-precision,automated plot seeding operations,significantly enhancing the accuracy and comparability of breeding experi-ments.